Pith. sign in

REVIEW 2 major objections 5 minor 148 references

MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance

T0 review · 2 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Interactive segmentation cuts clicks by 36% as context grows

desk verdict A well-run empirical paper that makes a real incremental advance—interactive segmentation with variable-size in-context context—and backs it with unusually broad evaluation, though the headline gains are measured with oracle-style simulated corrections and would be stronger with human-user evidence. read the letter →

arxiv 2412.15058 v2 pith:ALAAOMQL submitted 2024-12-19 cs.CV cs.LGeess.IV

Hallee E. Wong , Jose Javier Gonzalez Ortiz , John Guttag , Adrian V. Dalca This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords interactivesegmentationin-contextlearningbiomedicalimagemedicalimagingdatasetannotationfoundationmodelclickefficiencycontextset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces MultiverSeg, a segmentation model that lets a user label a new biomedical dataset image by image, with no pre-existing labels for that task. As each image is finished, it joins a context set that conditions the next prediction, so the number of clicks or scribbles needed to reach 90% Dice falls, often to zero. The authors report a 36% reduction in total clicks and a 25% reduction in scribble steps compared with ScribblePrompt on the first 18 images of unseen tasks. The contribution is a way to amortize annotation effort across a dataset rather than paying full interactive cost per image.

What carries the argument

The model is a UNet-style encoder-decoder in which every convolutional block is replaced by a CrossBlock. A CrossBlock takes the target image features and the context set features, uses a cross-convolution to pair the target with each context feature, then aggregates through an average and layer-normalized convolution to update both target and context representations. This lets a variable-size context set interact with the target at every scale. When the context is empty, a dummy entry is used, and the first image is segmented by a pretrained ScribblePrompt-UNet; afterward, each completed segmentation is appended to the context set.

What would settle it

Run a human-in-the-loop study where annotators interactively segment the same 18-image sets with MultiverSeg and ScribblePrompt, placing their own correction clicks; if the per-image click counts do not fall with context size or the 36% reduction disappears, the central claim fails. A cheaper probe: replace center-of-error clicks with clicks at random locations in the error region; if the advantage vanishes, the result is an artifact of the simulation.

Watch

Extended reading notes

Core claim

The central claim is that interactive segmentation can be performed in context: a single network consumes the target image, whatever user interactions it has received, and a variably sized set of previously segmented image–segmentation pairs, and improves as that set grows. On 161 held-out tasks from eight unseen datasets, the number of interactions required to reach a 90% Dice target decreases with each additional completed image, and the total interaction budget over the first 18 images is 36.41% ± 1.33% lower for clicks and 25.26% ± 1.80% lower for scribble steps than the ScribblePrompt baseline.

Load-bearing premise

The interaction counts rely on an oracle-like simulation where corrections are always placed at the center of the largest ground-truth error region and scribbles are drawn from ground-truth centerlines, which may not match how human annotators actually correct a model.

Editorial extensions

If this is right

  • Total annotation effort for a new dataset falls as context grows: per-image interactions decrease, often to zero, so the cost curve flattens rather than scaling linearly with image count.
  • Reaching 90% Dice on the first 18 images takes about 36% fewer clicks and 25% fewer scribble steps than the strongest per-image interactive baseline.
  • Accuracy on later images is correlated with earlier predictions, and thresholding previous predictions at 0.5 before adding them to the context improves subsequent accuracy.
  • No retraining is needed: the same weights segment unseen tasks at inference time, with runtime below 150 ms even with a 64-example context set.
  • Larger context sets give diminishing returns; one additional correction step typically buys more Dice than doubling the context size.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, if human corrections follow the simulated oracle pattern, MultiverSeg-style amortization could turn one-pass annotation of a large medical cohort into labeling a few images and then verifying automated predictions.
  • The authors' protocol assumption is testable: a user study with real clinicians placing corrections would show whether the 36% click reduction survives noisy, non-oracle interaction placement.
  • Context selection is the natural next lever: choosing which completed images to feed as context, rather than including all of them, could cut interaction counts further on heterogeneous datasets.
  • The same in-context amortization idea could be applied to other interactive prediction tasks, such as detection or registration, where previous outputs constrain the next input.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper proposes MultiverSeg, a UNet-like interactive segmentation network that takes user interactions on the target image together with a variable-size context set of previously segmented image-label pairs, produced sequentially by the user as they segment a new dataset. For the first image of a new task, the method falls back to a pre-trained ScribblePrompt; for subsequent images, the context set grows and the network is expected to segment with progressively fewer interactions. The paper contributes synthetic task generation via superpixel-derived labels with aggressive augmentation, a training loop with simulated interaction steps, and an evaluation on 12 held-out biomedical datasets (161 tasks with at least 18 test examples) comparing against interactive, in-context, and hybrid baselines. The reported central result is that MultiverSeg reduces the number of clicks by 36% and scribble steps by 25% to reach 90% Dice relative to ScribblePrompt on the first 18 images of unseen tasks.

Significance. If the result holds, MultiverSeg addresses a real bottleneck in biomedical annotation: dataset-level segmentation effort that scales sublinearly with the number of images. The paper is strong on breadth: 161 unseen tasks, 200 simulation rounds, several independent baselines (including a fine-tuning baseline and a hybrid SP+UVS), ablations of context-set quality, and a resolution sensitivity analysis. Code and weights are released, and the main result is an empirical measurement on held-out data rather than a derivation, so there is no internal circularity. The main risk to the headline claim is not fairness across methods but the realism of the simulated interaction protocol, since all methods are evaluated with ground-truth-guided correction placement; a robustness analysis under noisier prompts would substantially increase confidence in the human-effort interpretation of the numbers.

major comments (2)
  1. [§5.3 / Algorithm 1] The headline interaction reductions (36% fewer clicks and 25% fewer scribble steps vs ScribblePrompt, Section 6.2) are measured under the 'Center Clicks' and 'Centerline Scribbles' protocols of Section 5.3, in which every correction is placed at the center of the largest component of the ground-truth error region. The same ground-truth-guided oracle is used in training (Algorithm 1, hψ(yt, ŷj−1)). Because the paper's central claim is about reducing human effort, this protocol's realism is load-bearing: a human user cannot know the error region with respect to ground truth and will place noisier, less optimal corrections. The paper provides no sensitivity analysis (e.g., perturbed click locations, random positive/negative clicks, or a small human study) showing that MultiverSeg's relative advantage is robust to such noise. I request such an analysis, or a clearly stated restriction of the claim to the oracle protocol.
  2. [§8 / §2 / Table 6] The conclusion states 'we introduce the first model that can perform interactive segmentation of biomedical images in context.' This is contradicted by the paper's own related work and Table 6, which classifies OnePrompt as both interactive and in-context (context size = 1) and describes in Appendix D.1 that OnePrompt supports interactive segmentation by using the same image as context and target. Please revise the novelty claim to the specific contribution — variable-size context sets with iterative corrections on the target image — or remove the 'first' claim.
minor comments (5)
  1. [Appendix E.4] The reported MultiverSeg numbers '4.64 ± 0.10 clicks or 4.64 ± 0.10 scribble steps per image' are identical, which is likely a typo; please correct the scribble-steps value.
  2. [§5.2 / §6.1] The text states the evaluation covers 12 held-out datasets and 187 tasks, while Section 6.1 reports results on 161 tasks from 8 datasets; the relationship between these counts should be stated explicitly.
  3. [§6.2] The sentence 'For larger sets of images, using MultiverSeg results in even greater reductions' is supported by Appendix E.2 figures but not quantified in the main text; consider giving a specific number for the 60-image setting shown in Figures 13–15.
  4. [§7 / §6] Experiment 2 is evaluated at 1282 resolution while Experiment 1 is at 2562; this is disclosed but the different resolutions make direct comparison of absolute Dice values across the two experiments difficult, so a short reminder in the Experiment 2 setup would help.
  5. [Abstract / §6.1] The abstract says 'without requiring access to any existing labeled data from that task or domain,' but Experiment 1 seeds the context with an image from the training split of the same dataset that is interactively labeled to 90% Dice; please clarify that the seed image is annotated by the user rather than drawn from pre-existing labels.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the 36%/25% interaction reductions are held-out empirical measurements; same-lab baselines and oracle-style click simulation raise fairness/validity concerns, not circular reductions.

full rationale

MultiverSeg's headline result — 36% fewer clicks and 25% fewer scribble steps to 90% Dice than ScribblePrompt — is an empirical measurement on 12 held-out test datasets (Section 6), averaged over 200 simulations with bootstrapped confidence intervals. The quantity being predicted (total interactions per dataset) is not a parameter fitted from the data that produced it; no fitted value is reused as a prediction. The architecture borrows the CrossBlock from UniverSeg [16] and uses the authors' own ScribblePrompt [130] for the first-image segmenter and as a comparison baseline, and SP+UVS combines two same-lab models. These are implementation and evaluation choices, not evidence that the measured reduction holds, and the comparison itself is against external test data; the same-lab origin does not make the outcome an identity by construction. The SP+UVS threshold (minimum context set size 5) and binary context predictions are selected on validation data; this is standard model selection and a potential fairness concern, not a circular reduction. The Prompt Simulation protocol (correction clicks at the center of the largest ground-truth error region, Algorithm 1 and Section 5.3) is an assumption about user behavior shared by all methods and used in training; it threatens external validity but does not define the measured interaction reduction in terms of itself. The paper's Limitations paragraph acknowledges weaker context benefit on heterogeneous datasets such as BUID, which is consistent with sensitivity to the simulation but is not circularity. I find no self-definitional step, no fitted-input-called-prediction step, no uniqueness theorem imported from authors, and no ansatz smuggled in via citation. Score 2 reflects only minor, non-load-bearing self-citations.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper is empirical; its central claim trades on the representativeness of the training corpora, on the simulated interaction protocol, and on the ability to replace ground-truth context labels with thresholded predictions at inference. The numbers cited in the abstract are averages over 200 simulation rounds, not fitted quantities. The only values tuned against held-out data are evaluation and baseline settings (binarization threshold, SP+UVS minimum context size), which do not enter the model itself.

free parameters (4)
  • Context set binarization threshold = 0.5
    Applied to predicted segmentations before they enter the context set for MultiverSeg and SP+UVS; the choice was made on validation data (Appendix E.2, Fig. 16) and improves accuracy for both methods.
  • SP+UVS minimum context set size = 5
    Below 5 context examples, the SP+UVS baseline ignores UniverSeg and uses only ScribblePrompt; selected on validation data (Section 5.4, Fig. 17) because UniverSeg hurts for small context sets.
  • Synthetic task probability psynth = 0.5
    Fraction of training examples replaced by synthetic superpixel tasks; chosen by hand in Section C.2. It shapes generalization but is not fitted to test results.
  • Context set size distribution U[0,64] = U[0,64]
    Training-time sampling from Section 5.1; hand-chosen and affects the model's ability to use large contexts.
assumptions (4)
  • domain assumption Training on 67 biomedical datasets plus synthetic tasks transfers to the 12 held-out evaluation datasets, including unseen modalities and labels.
    The entire evaluation claims generalization to new tasks; this is the core inductive assumption of the paper (Section 5.2).
  • domain assumption Simulated center clicks and centerline scribbles, with corrections placed in the largest ground-truth error region, approximate real user interactions.
    Section 5.3 defines the inference-time interaction protocols and Algorithm 1 simulates training corrections from ground truth. The measured interaction counts depend on this proxy.
  • domain assumption Thresholded predicted segmentations can stand in for ground-truth labels in the context set at inference.
    The model is trained with ground-truth context, but deployed with its own predictions; the paper tunes the 0.5 threshold on validation data (Section 6.2, Appendix E.2).
  • domain assumption 90% Dice is an appropriate accuracy target because fully-supervised nnUNet models average 88.67 Dice on the same test data.
    Section 6.1 uses the nnUNet upper bound to justify the target; if the target were lower or higher, interaction counts would change.

how reviews work

0 comments
Cite this review

Pith. "Pith review of MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance." pith.science (2026). https://pith.science/paper/ALAAOMQL

@misc{pith2026241215058,
  author       = {Pith},
  title        = {Pith review of: MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ALAAOMQL}},
  note         = {Machine review of arXiv:2412.15058}
}
read the original abstract

Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive, requiring substantial human effort for each image, or require an existing set of previously labeled images. We introduce a system, MultiverSeg, that enables practitioners to rapidly segment an entire new dataset without requiring access to any existing labeled data from that task or domain. Along with the image to segment, the model takes user interactions such as clicks, bounding boxes or scribbles as input, and predicts a segmentation. As the user segments more images, those images and segmentations become additional inputs to the model, providing context. As the context set of labeled images grows, the number of interactions required to segment each new image decreases. We demonstrate that MultiverSeg enables users to interactively segment new datasets efficiently, by amortizing the number of interactions per image to achieve an accurate segmentation. Compared to using a state-of-the-art interactive segmentation method, MultiverSeg reduced the total number of clicks by 36% and scribble steps by 25% to achieve 90% Dice on sets of images from unseen tasks. We release code and model weights at https://multiverseg.csail.mit.edu

Figures

Figures reproduced from arXiv: 2412.15058 by the authors.

Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. MultiverSeg Architecture. The MultiverSeg network (left) takes as input a stack of target image inputs qi and a context set of image-segmentation pairs {(xl, yl)} m l=1. The target image inputs include a target image xi, optional user interactions ui,j , and a previous predicted segmentation yˆi,j−1, if available. The architecture is similar to a UNet [109]. However, we use a CrossBlock [16] (right) with additional … view at source ↗
Figure 3
Figure 3. Interactions to target Dice on unseen tasks. Num￾ber of interactions needed to reach a 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. MultiverSeg requires substantially fewer number of interactions to achieve 90% Dice than the baselines, and as more images are segmented, the average number of interactions required decreases dramatical… view at source ↗
Figures from the paper (22 more)
Figure 4
Figure 4. Figure 4: Interactions per image by unseen dataset. We show average number of clicks and scribble steps per image to segment 18 images to ≥ 90% Dice for each method. In all scenarios, Mul￾tiverSeg required fewer or the same number of interactions than the best baseline. Error ba…
Figure 5
Figure 5. Figure 5: Example predictions after 1 interaction step. We show predictions for MultiverSeg and the top two performing baselines on a randomly chosen example from each held-out task. We use a context set of 10 examples that were previously segmented to ≥90% Dice. For each method…
Figure 6
Figure 6. Figure 6: In-context segmentation performance across context set sizes. We compare MultiverSeg to an in-context segmentation method, UniverSeg [16], given ground truth context labels. Shad￾ing shows 95% CI from bootstrapping. the BUID dataset until the context set has ≥ 5 exampl…
Figure 8
Figure 8. Figure 8: Synthetic task generation example. Given an input image, we apply a superpixel algorithm to generate a superpixel map of potential synthetic labels. We randomly sample one of the superpixels to serve as a synthetic label. Next, we duplicate the input image and syntheti…
Figure 9
Figure 9. Figure 9: Examples per task. We visualize the distribution of examples per task in our validation data. We only consider tasks with at least 18 examples in Experiment 1. text set improved performance ( [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: Example segmentation process with MultiverSeg. We begin by interactively segmenting a seed image (Example 0) to 90% Dice. The Example 0 image and final prediction are added to the context set for subsequent examples. For each subsequent example, we first make an initi…
Figure 11
Figure 11. Figure 11: Clicks to target Dice on unseen datasets. Number of interactions needed to reach 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. MultiverSeg requires substantially fewer interactions to …
Figure 12
Figure 12. Figure 12: Scribbles to target Dice on unseen datasets. Number of interactions needed to reach 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. MultiverSeg requires substantially fewer interactions …
Figure 13
Figure 13. Figure 13: Scribble steps to target Dice by task for WBC. Num￾ber of interactions needed to reach a 90% Dice as a function of the example number being segmented. For the n th image being seg￾mented, the context set has n examples. Shading shows 95% CI from bootstrapping. WBC [14…
Figure 15
Figure 15. Figure 15: Center clicks to target Dice by task for HipXRay. Number of interactions needed to reach 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. Shading shows 95% CI from bootstrapping. HipXRay …
Figure 14
Figure 14. Figure 14 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]
Figure 16
Figure 16. Figure 16: Interactions to target dice on unseen datasets with different types of context sets. Number of interactions needed to reach a 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. We show resu…
Figure 17
Figure 17. Figure 17: Variations of SP+UVS. Number of interactions needed to reach a 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. We show results for SP+UVS with different minimum context set size cutoffs,…
Figure 18
Figure 18. Figure 18: Average segmentation quality and total interactions per unseen task. We measure average segmentation quality across a set of 18 test images using Dice score and 95th percentile Hausdorff distance (HD95). For each metric, we show mean and standard deviation from bootst…
Figure 19
Figure 19. Figure 19: Bootstrapping UniverSeg. We use UniverSeg to sequentially segment images starting from a single example with a ground truth segmentation. After segmenting each image, the image and predicted segmentation are added to the context set for the next example. For the “orac…
Figure 20
Figure 20. Figure 20: Bootstrapping UniverSeg results by dataset. We show Dice score vs. example number for unseen tasks averaged by dataset. After segmenting each image, the image and predicted segmentation are added to the context set for the next example. For the “oracle” version, we us…
Figure 21
Figure 21. Figure 21: MultiverSeg outperforms task-specific fine-tuning on most datasets. We show average number of clicks and scribble steps per image to segment 18 images to ≥ 90% Dice for each method. For FT ScribblePrompt (shaded), we used ScribblePrompt to interactively segment 5 imag…
Figure 22
Figure 22. Figure 22: Interactions to target Dice on unseen tasks at 1282 resolution. Number of interactions needed to reach a 90% Dice as a function of the example number being segmented. For the n th image being segmented, the context set has n examples. MultiverSeg requires substantiall…
Figure 23
Figure 23. Figure 23: Interactions per image by unseen dataset at 1282 resolution. We show average number of clicks and scribble steps per image to segment 18 images to ≥ 90% Dice for each method. In all scenarios, MultiverSeg required fewer or the same number of interactions than the best…
Figure 24
Figure 24. Figure 24: In-context segmentation performance across context set sizes on unseen datasets. We compare MultiverSeg to UniverSeg, an in-context segmentation method, given ground truth context labels. Points show results for context set sizes 1, 2, 4, 8, 16, 32, 64, 96, 128 and 25…
Figure 25
Figure 25. Figure 25: Interactive segmentation in context with center clicks on unseen datasets. MultiverSeg’s interactive segmentation perfor￾mance with the same number of interactions improves as the context set size grows. We first make an initial prediction based on the context set (st…
Figure 26
Figure 26. Figure 26: Interactive segmentation in context with centerline scribbles on unseen datasets. MultiverSeg’s interactive segmentation performance with the same number of interactions improves as the context set size grows. We first make an initial prediction based on the context s…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

148 extracted references · 60 canonical work pages

  1. [1]

    Automatic segmentation of mandible in panoramic x-ray.Journal of Medical Imaging, 2(4):044003,

    Amir Hossein Abdi, Shohreh Kasaei, and Mojdeh Mehdizadeh. Automatic segmentation of mandible in panoramic x-ray.Journal of Medical Imaging, 2(4):044003,

  2. [2]

    C. J. Aine, H. J. Bockholt, J. R. Bustillo, J. M. Ca ˜nive, A. Caprihan, C. Gasparovic, F. M. Hanlon, J. M. Houck, R. E. Jung, J. Lauriello, J. Liu, A. R. Mayer, N. I. Perrone- Bizzozero, S. Posse, J. M. Stephen, J. A. Turner, V . P. Clark, and Vince D. Calhoun. Multimodal Neuroimaging in Schizophrenia: Description and Dissemination. Neuroin- formatics, 1...

    2017

  3. [3]

    Dataset of breast ultrasound images

    Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Aly Fahmy. Dataset of breast ultrasound images. Data in Brief, 28:104863, 2020. 4, 5, 6, 7, 18, 20, 26

    2020

  4. [4]

    ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

    Muhammad Asad, Lucas Fidon, and Tom Vercauteren. ECONet: Efficient Convolutional Online Likelihood Net- work for Scribble-based Interactive Segmentation. InInter- national Conference on Medical Imaging with Deep Learn- ing, pages 35–47. PMLR, 2022. arXiv:2201.04584 [cs, eess]. 2

    work page Pith review arXiv 2022

  5. [5]

    Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation

    Muhammad Asad, Helena Williams, Indrajeet Mandal, Sarim Ather, Jan Deprest, Jan D’hooge, and Tom Ver- cauteren. Adaptive Multi-scale Online Likelihood Net- work for AI-assisted Interactive Segmentation. In Inter- national Conference on Medical Image Computing and Computer-Assisted Intervention , pages 564–574, 2023. arXiv:2303.13696 [cs, eess]. 2

    work page Pith review arXiv 2023

  6. [6]

    Layer normalization

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450 ,

    arXiv

  7. [7]

    The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification

    Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C Kitamura, Sarthak Pati, et al. The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314, 2021. 4, 19

    arXiv 2021

  8. [8]

    Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features

    Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S Kirby, John B Freymann, Keyvan Farahani, and Christos Davatzikos. Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features. Scientific data, 4 (1):1–13, 2017. 19

    2017

Show all 148 references
  1. [9]

    Deep placental vessel segmentation for fetoscopic mosaicking

    Sophia Bano, Francisco Vasconcelos, Luke M Shepherd, Emmanuel Vander Poorten, Tom Vercauteren, Sebastien Ourselin, Anna L David, Jan Deprest, and Danail Stoy- anov. Deep placental vessel segmentation for fetoscopic mosaicking. In Medical Image Computing and Computer Assisted I...

    2020

  2. [10]

    Large-scale interactive object segmentation with human an- notators

    Rodrigo Benenson, Stefan Popov, and Vittorio Ferrari. Large-scale interactive object segmentation with human an- notators. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 11700– 11709, 2019. 5

    2019

  3. [11]

    Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging , 37(11):2514–2525, 2018

    Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is t...

    2018

  4. [12]

    The liver tumor segmentation benchmark (lits)

    Patrick Bilic, Patrick Ferdinand Christ, Eugene V orontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, J ¨urgen Hesser, et al. The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:1901.04056, 2019. 4, 19

    1901 arXiv

  5. [13]

    Synthseg: Segmenta- tion of brain mri scans of any contrast and resolution with- out retraining

    Benjamin Billot, Douglas N Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V Dalca, Juan Eugenio Iglesias, et al. Synthseg: Segmenta- tion of brain mri scans of any contrast and resolution with- out retraining. Medical image analysis, 86:102789, 2023. 4

    2023

  6. [14]

    Nci-isbi 2013 challenge: automated segmentation of prostate structures

    Nicholas Bloch, Anant Madabhushi, Henkjan Huisman, John Freymann, Justin Kirby, Michael Grauer, Andinet Enquobahrie, Carl Jaffe, Larry Clarke, and Keyvan Fara- hani. Nci-isbi 2013 challenge: automated segmentation of prostate structures. The Cancer Imaging Archive, 370(6):5,

    2013

  7. [15]

    Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learn- ing algorithm

    Mateusz Buda, Ashirbani Saha, and Maciej A Mazurowski. Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learn- ing algorithm. Computers in biology and medicine , 109: 218–225, 2019. 19

    2019

  8. [16]

    Sabuncu, John Guttag, and Adrian V

    Victor Ion Butoi*, Jose Javier Gonzalez Ortiz*, Tianyu Ma, Mert R. Sabuncu, John Guttag, and Adrian V . Dalca. Uni- verseg: Universal medical image segmentation. In ICCV,

  9. [17]

    Caicedo, Allen Goodman, Kyle W

    Juan C. Caicedo, Allen Goodman, Kyle W. Karhohs, Beth A. Cimini, Jeanelle Ackerman, Marzieh Haghighi, CherKeng Heng, Tim Becker, Minh Doan, Claire McQuin, Mohammad Rohban, Shantanu Singh, and Anne E. Carpen- ter. Nucleus segmentation across imaging experiments: the 2018 Data S...

    2018

  10. [18]

    An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy.PLoS biology, 8(10):e1000502, 2010

    Albert Cardona, Stephan Saalfeld, Stephan Preibisch, Ben- jamin Schmid, Anchi Cheng, Jim Pulokas, Pavel Toman- cak, and V olker Hartenstein. An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy.PLoS...

    2010

  11. [19]

    SAM-Med2D, 2023

    Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong 9 Chen, Lei Jiang, Hui Sun, Junjun He, Shaoting Zhang, Min Zhu, and Yu Qiao. SAM-Med2D, 2023. arXiv:2308.16184 [cs]. 1, 5, 22

    2023 arXiv

  12. [20]

    Interactive medical image segmentation: A bench- mark dataset and baseline

    Junlong Cheng, Bin Fu, Jin Ye, Guoan Wang, Tianbin Li, Haoyu Wang, Ruoyu Li, He Yao, Junren Cheng, JingWen Li, et al. Interactive medical image segmentation: A bench- mark dataset and baseline. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 208...

    2025

  13. [21]

    Noel C. F. Codella, David A. Gutman, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Stephen W. Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin K. Mishra, Har- ald Kittler, and Allan Halpern. Skin lesion analysis to- ward melanoma detection: A challenge at the 2017 interna...

    2017 arXiv

  14. [22]

    Neuralizer: General neuroimage analysis without re-training

    Steffen Czolbe and Adrian V Dalca. Neuralizer: General neuroimage analysis without re-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6217–6230, 2023. 2

    2023

  15. [23]

    Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation

    Adrian V Dalca, John Guttag, and Mert R Sabuncu. Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 9290–9299, 2018. 19

    2018

  16. [24]

    Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation

    Adrian V Dalca, John Guttag, and Mert R Sabuncu. Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 9290–9299, 2018. 18

    2018

  17. [25]

    Label anything: Multi-class few- shot semantic segmentation with visual prompts

    Pasquale De Marinis, Nicola Fanelli, Raffaele Scaringi, Emanuele Colonna, Giuseppe Fiameni, Gennaro Vessio, and Giovanna Castellano. Label anything: Multi-class few- shot semantic segmentation with visual prompts. arXiv preprint arXiv:2407.02075, 2024. 2, 5, 17, 22

    2024 arXiv

  18. [26]

    Teleophta: Machine learning and image process- ing methods for teleophthalmology

    Etienne Decenciere, Guy Cazuguel, Xiwei Zhang, Guil- laume Thibault, J-C Klein, Fernand Meyer, Beatriz Mar- cotegui, Gw´enol´e Quellec, Mathieu Lamard, Ronan Danno, et al. Teleophta: Machine learning and image process- ing methods for teleophthalmology. Irbm, 34(2):196–203,

  19. [27]

    Early detection of myocardial infarction in low- quality echocardiography

    Aysen Degerli, Morteza Zabihi, Serkan Kiranyaz, Tahir Hamid, Rashid Mazhar, Ridha Hamila, and Moncef Gab- bouj. Early detection of myocardial infarction in low- quality echocardiography. IEEE Access, 9:34442–34453,

  20. [28]

    Monai label: A framework for ai-assisted interactive label- ing of 3d medical images

    Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath, Yucheng Tang, Alvin Ihsani, Muhammad Asad, Fernando P´erez-Garc´ıa, Pritesh Mehta, Wenqi Li, Mona Flores, et al. Monai label: A framework for ai-assisted interactive label- ing of 3d medical images. Medical Image Analysis , 9...

    2024

  21. [29]

    Measures of the amount of ecologic association between species

    Lee R Dice. Measures of the amount of ecologic association between species. Ecology, 26(3):297–302, 1945. 5, 6

    1945

  22. [30]

    Efficient graph-based image segmentation

    Pedro F Felzenszwalb and Daniel P Huttenlocher. Efficient graph-based image segmentation. International journal of computer vision, 59:167–181, 2004. 5, 17

    2004

  23. [31]

    Freesurfer

    Bruce Fischl. Freesurfer. Neuroimage, 62(2):774–781,

  24. [32]

    Pan- nuke dataset extension, insights and baselines

    J Gamper, NA Koohbanani, K Benes, S Graham, M Jahan- ifar, SA Khurram, A Azam, K Hewitt, and N Rajpoot. Pan- nuke dataset extension, insights and baselines. arxiv. 2020 doi: 10.48550. ARXIV, 2003. 4, 19

    2020

  25. [33]

    Boosting your context by dual similarity checkup for in-context learning medical im- age segmentation

    Jun Gao, Qicheng Lao, Qingbo Kang, Paul Liu, Chenlin Du, Kang Li, and Le Zhang. Boosting your context by dual similarity checkup for in-context learning medical im- age segmentation. IEEE Transactions on Medical Imaging,

  26. [34]

    Segmented anisotropic ssTEM dataset of neural tissue

    Stephan Gerhard, Jan Funke, Julien Martel, Albert Car- dona, and Richard Fetter. Segmented anisotropic ssTEM dataset of neural tissue. 2013. 4, 19

    2013

  27. [35]

    The mcic collection: a shared repository of multi- modal, multi-site brain image data from a clinical inves- tigation of schizophrenia

    Randy L Gollub, Jody M Shoemaker, Margaret D King, Tonya White, Stefan Ehrlich, Scott R Sponheim, Vincent P Clark, Jessica A Turner, Bryon A Mueller, Vince Magnotta, et al. The mcic collection: a shared repository of multi- modal, multi-site brain image data from a clinical in...

  28. [36]

    Synthetic data in generalizable, learning-based neuroimaging

    Karthik Gopinath, Andrew Hoopes, Daniel C Alexander, Steven E Arnold, Yael Balbastre, Benjamin Billot, Adri `a Casamitjana, You Cheng, Russ Yue Zhi Chua, Brian L Ed- low, et al. Synthetic data in generalizable, learning-based neuroimaging. Imaging Neuroscience, 2:1–22, 2024. 4

    2024

  29. [37]

    Embar- rassingly simple scribble supervision for 3d medical seg- mentation

    Karol Gotkowski, Carsten L ¨uth, Paul F J ¨ager, Sebastian Ziegler, Lars Kr ¨amer, Stefan Denner, Shuhan Xiao, Nico Disch, Klaus H Maier-Hein, and Fabian Isensee. Embar- rassingly simple scribble supervision for 3d medical seg- mentation. arXiv preprint arXiv:2403.12834, 2024. 2

    2024 arXiv

  30. [38]

    Automatic segmentation of brain mris of 2-year-olds into 83 regions of interest

    Ioannis S Gousias, Daniel Rueckert, Rolf A Heckemann, Leigh E Dyet, James P Boardman, A David Edwards, and Alexander Hammers. Automatic segmentation of brain mris of 2-year-olds into 83 regions of interest. Neuroim- age, 40(2):672–684, 2008. 19

    2008

  31. [39]

    Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants

    Ioannis S Gousias, A David Edwards, Mary A Ruther- ford, Serena J Counsell, Jo V Hajnal, Daniel Rueckert, and Alexander Hammers. Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants. Neuroimage, 62(3):1499– 1...

    2012

  32. [40]

    Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images

    Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, and Nasir Rajpoot. Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images. Medical Image Analysis, 58:101563, 2019. 4

    2019

  33. [41]

    Deep learning enables automatic detection and segmentation of brain metastases on multisequence mri

    Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Daniel Rubin, and Greg Zaharchuk. Deep learning enables automatic detection and segmentation of brain metastases on multisequence mri. Journal of Magnetic Resonance Imaging, 51(1):175–182, 2020. 19

    2020

  34. [42]

    X-ray images of the hip joints

    Daniel Gut. X-ray images of the hip joints. 1, 2021. Pub- lisher: Mendeley Data. 4, 5, 6, 7, 18, 20, 26

    2021

  35. [43]

    The state of the 10 art in kidney and kidney tumor segmentation in contrast- enhanced ct imaging: Results of the kits19 challenge

    Nicholas Heller, Fabian Isensee, Klaus H Maier-Hein, Xi- aoshuai Hou, Chunmei Xie, Fengyi Li, Yang Nan, Guan- grui Mu, Zhiyong Lin, Miofei Han, et al. The state of the 10 art in kidney and kidney tumor segmentation in contrast- enhanced ct imaging: Results of the kits19 challe...

    2020

  36. [44]

    Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset

    Moritz R Hernandez Petzsche, Ezequiel de la Rosa, Uta Hanning, Roland Wiest, Waldo Valenzuela, Mauricio Reyes, Maria Meyer, Sook-Lei Liew, Florian Kofler, Ivan Ezhov, et al. Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific da...

    2022

  37. [45]

    Greve, Bruce Fischl, John Guttag, and Adrian V

    Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John Guttag, and Adrian V . Dalca. Learning the effect of registration hyperparameters with hypermorph. Machine Learning for Biomedical Imaging , 1:1–30, 2022. 19

    2022

  38. [46]

    Locating blood vessels in retinal images by piece- wise threshold probing of a matched filter response

    AD Hoover, Valentina Kouznetsova, and Michael Gold- baum. Locating blood vessels in retinal images by piece- wise threshold probing of a matched filter response. IEEE Transactions on Medical imaging, 19(3):203–210, 2000. 4, 5, 19

    2000

  39. [47]

    Icl-sam: Synergizing in-context learn- ing model and sam in medical image segmentation

    Jiesi Hu, Yang Shang, Yanwu Yang, Guo Xutao, Hanyang Peng, and Ting Ma. Icl-sam: Synergizing in-context learn- ing model and sam in medical image segmentation. InMed- ical Imaging with Deep Learning, 2024. 2

    2024

  40. [48]

    How to efficiently adapt large segmentation model(sam) to medical images,

    Xinrong Hu, Xiaowei Xu, and Yiyu Shi. How to efficiently adapt large segmentation model(sam) to medical images,

  41. [49]

    Interformer: Real-time interactive image segmentation

    You Huang, Hao Yang, Ke Sun, Shengchuan Zhang, Liu- juan Cao, Guannan Jiang, and Rongrong Ji. Interformer: Real-time interactive image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22301–22311, 2023. 5

    2023

  42. [50]

    Matchseg: Towards better segmentation via reference image matching

    Jiayu Huo, Ruiqiang Xiao, Haotian Zheng, Yang Liu, Se- bastien Ourselin, and Rachel Sparks. Matchseg: Towards better segmentation via reference image matching. arXiv preprint arXiv:2403.15901, 2024. 8

    2024 arXiv

  43. [51]

    Klanderman, and William J Rucklidge

    Daniel P Huttenlocher, Gregory A. Klanderman, and William J Rucklidge. Comparing images using the haus- dorff distance. IEEE Transactions on pattern analysis and machine intelligence, 15(9):850–863, 1993. 6

    1993

  44. [52]

    Teeth segmentation dataset

    Humans in the Loop. Teeth segmentation dataset. 4, 19

  45. [53]

    Jaeger, Simon A

    Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen, and Klaus H. Maier-Hein. nnU-Net: a self- configuring method for deep learning-based biomedical im- age segmentation. Nature Methods, 18(2):203–211, 2021. 2, 5

    2021

  46. [54]

    Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation.arXiv preprint arXiv:2206.08023, 2022

    Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xi- ang Wan, et al. Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation.arXiv preprint arXiv:2206.08023, 2022. 4, 19

    2022 arXiv

  47. [55]

    Rashed Karim, R James Housden, Mayuragoban Balasub- ramaniam, Zhong Chen, Daniel Perry, Ayesha Uddin, Yosra Al-Beyatti, Ebrahim Palkhi, Prince Acheampong, Saman- tha Obom, et al. Evaluation of current algorithms for seg- mentation of scar tissue from late gadolinium enhancemen...

    2013

  48. [56]

    Alper Selver, O ˘guz Dicle, Mustafa Barıs ¸, and N

    Ali Emre Kavur, M. Alper Selver, O ˘guz Dicle, Mustafa Barıs ¸, and N. Sinem Gezer. CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data,

  49. [57]

    Emre Kavur, N

    A. Emre Kavur, N. Sinem Gezer, Mustafa Barıs ¸, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savas ¸ ¨Ozkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ron- nie ...

    2021

  50. [58]

    Evaluation and improvement of segment anything model for interactive histopathology image segmentation,

    SeungKyu Kim, Hyun-Jic Oh, Seonghui Min, and Won-Ki Jeong. Evaluation and improvement of segment anything model for interactive histopathology image segmentation,

  51. [59]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 ,

    arXiv

  52. [60]

    Left ventricular wall motion estimation by ac- tive polynomials for acute myocardial infarction detection

    Serkan Kiranyaz, Aysen Degerli, Tahir Hamid, Rashid Mazhar, Rayyan El Fadil Ahmed, Rayaan Abouhasera, Morteza Zabihi, Junaid Malik, Ridha Hamila, and Moncef Gabbouj. Left ventricular wall motion estimation by ac- tive polynomials for acute myocardial infarction detection. IEEE...

    2020

  53. [61]

    Berg, Wan-Yen Lo, Piotr Doll´ar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Doll´ar, and Ross Girshick. Segment anything. In ICCV, 2023. 1, 2, 3, 5, 17, 22

    2023

  54. [62]

    Continuous adaptation for interac- tive object segmentation by learning from corrections

    Theodora Kontogianni, Michael Gygli, Jasper Uijlings, and Vittorio Ferrari. Continuous adaptation for interac- tive object segmentation by learning from corrections. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI ...

    2020

  55. [63]

    Tracked 3d ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry

    Markus Kr ¨onke, Christine Eilers, Desislava Dimova, Melanie K¨ohler, Gabriel Buschner, Lilit Schweiger, Lemo- nia Konstantinidou, Marcus Makowski, James Nagarajah, Nassir Navab, et al. Tracked 3d ultrasound and deep neural network-based thyroid segmentation reduce interobserv...

  56. [64]

    Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge

    Hugo J Kuijf, J Matthijs Biesbroek, Jeroen De Bresser, Rutger Heinen, Simon Andermatt, Mariana Bento, Matt Berseth, Mikhail Belyaev, M Jorge Cardoso, Adria Casamitjana, et al. Standardized assessment of automatic segmentation of white matter hyperintensities and results of the...

    2019

  57. [65]

    A dynamic 4d probabilistic atlas of 11 the developing brain.NeuroImage, 54(4):2750–2763, 2011

    Maria Kuklisova-Murgasova, Paul Aljabar, Latha Srini- vasan, Serena J Counsell, Valentina Doria, Ahmed Serag, Ioannis S Gousias, James P Boardman, Mary A Rutherford, A David Edwards, et al. A dynamic 4d probabilistic atlas of 11 the developing brain.NeuroImage, 54(4):2750–2763...

    2011

  58. [66]

    Segthor: segmentation of thoracic organs at risk in ct images

    Zo ´e Lambert, Caroline Petitjean, Bernard Dubray, and Su Kuan. Segthor: segmentation of thoracic organs at risk in ct images. In 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) , pages 1–6. IEEE, 2020. 4, 19

    2020

  59. [67]

    Discobox: Weakly supervised instance segmentation and semantic correspondence from box su- pervision

    Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Rad- hakrishnan, Guilin Liu, Yuke Zhu, Larry S Davis, and An- ima Anandkumar. Discobox: Weakly supervised instance segmentation and semantic correspondence from box su- pervision. In Proceedings of the IEEE/CVF International ...

    2021

  60. [68]

    Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

    Bennett Landman, Zhoubing Xu, J Igelsias, Martin Styner, T Langerak, and Arno Klein. Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault Work- shop Challenge, page 12, 2015. 4, 5, 17, 18, 19

    2015

  61. [69]

    Deep learning for segmentation using an open large-scale dataset in 2d echocardiography

    Sarah Leclerc, Erik Smistad, Joao Pedrosa, Andreas Østvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Pierre-Marc Jodoin, Thomas Gre- nier, et al. Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEE...

    2019

  62. [70]

    Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review

    Guillaume Lema ˆıtre, Robert Mart´ı, Jordi Freixenet, Joan C Vilanova, Paul M Walker, and Fabrice Meriaudeau. Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review. Com- puters in biology and medicine, 60:8–31, 2015. 4, 19

    2015

  63. [71]

    Ipn-v2 and octa-500: Methodology and dataset for retinal image segmentation

    Mingchao Li, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, and Qiang Chen. Ipn-v2 and octa-500: Methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261, 2020. 4, 19

    2012 arXiv

  64. [72]

    Scribblevc: Scribble-supervised medical im- age segmentation with vision-class embedding

    Zihan Li, Yuan Zheng, Xiangde Luo, Dandan Shan, and Qingqi Hong. Scribblevc: Scribble-supervised medical im- age segmentation with vision-class embedding. InProceed- ings of the 31st ACM International Conference on Multime- dia, pages 3384–3393, 2023. 2

    2023

  65. [73]

    Scribblesup: Scribble-supervised convolutional networks for semantic segmentation

    Di Lin, Jifeng Dai, Jiaya Jia, Kaiming He, and Jian Sun. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. InProceedings of the IEEE con- ference on computer vision and pattern recognition , pages 3159–3167, 2016. 2

    2016

  66. [74]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll ´ar. Focal loss for dense object detection. In Proceedings of the IEEE international conference on com- puter vision, pages 2980–2988, 2017. 5

    2017

  67. [75]

    Samus: Adapting segment any- thing model for clinically-friendly and generalizable ultra- sound image segmentation, 2023

    Xian Lin, Yangyang Xiang, Li Zhang, Xin Yang, Zengqiang Yan, and Li Yu. Samus: Adapting segment any- thing model for clinically-friendly and generalizable ultra- sound image segmentation, 2023. 2

    2023

  68. [76]

    Evaluation of prostate segmentation algorithms for mri: the promise12 challenge

    Geert Litjens, Robert Toth, Wendy van de Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram van Ginneken, Graham Vin- cent, Gwenael Guillard, Neil Birbeck, Jindang Zhang, et al. Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Medical image analysis, 18...

    2014

  69. [77]

    Clickseg: 3d instance segmentation with click-level weak annotations

    Leyao Liu, Tao Kong, Minzhao Zhu, Jiashuo Fan, and Lu Fang. Clickseg: 3d instance segmentation with click-level weak annotations. arXiv preprint arXiv:2307.09732, 2023. 2

    2023 arXiv

  70. [78]

    Simpleclick: Interactive image segmentation with simple vision transformers

    Qin Liu, Zhenlin Xu, Gedas Bertasius, and Marc Ni- ethammer. Simpleclick: Interactive image segmentation with simple vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 22290–22300, 2023. 2, 5

    2023

  71. [79]

    Rethinking interactive image segmentation with low latency high quality and diverse prompts

    Qin Liu, Jaemin Cho, Mohit Bansal, and Marc Nietham- mer. Rethinking interactive image segmentation with low latency high quality and diverse prompts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 3773–3782, 2024. 2, 5, 22

    2024

  72. [80]

    The effects of interactive la- tency on exploratory visual analysis

    Zhicheng Liu and Jeffrey Heer. The effects of interactive la- tency on exploratory visual analysis. IEEE transactions on visualization and computer graphics , 20(12):2122–2131,

  73. [81]

    One thing one click: A self-training approach for weakly supervised 3d semantic segmentation

    Zhengzhe Liu, Xiaojuan Qi, and Chi-Wing Fu. One thing one click: A self-training approach for weakly supervised 3d semantic segmentation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1726–1736, 2021. 2

    2021

  74. [82]

    Annotated high-throughput microscopy image sets for validation

    Vebjorn Ljosa, Katherine L Sokolnicki, and Anne E Car- penter. Annotated high-throughput microscopy image sets for validation. Nature methods, 9(7):637–637, 2012. 4, 19

    2012

  75. [83]

    A vertebral segmentation dataset with fracture grading

    Maximilian T L ¨offler, Anjany Sekuboyina, Alina Jacob, Anna-Lena Grau, Andreas Scharr, Malek El Husseini, Mareike Kallweit, Claus Zimmer, Thomas Baum, and Jan S Kirschke. A vertebral segmentation dataset with fracture grading. Radiology: Artificial Intelligence , 2(4):e190138,

  76. [84]

    Word: Revisiting organs segmentation in the whole abdom- inal region

    Xiangde Luo, Wenjun Liao, Jianghong Xiao, Tao Song, Xi- aofan Zhang, Kang Li, Guotai Wang, and Shaoting Zhang. Word: Revisiting organs segmentation in the whole abdom- inal region. arXiv preprint arXiv:2111.02403, 2021. 4, 19

    2021 arXiv

  77. [85]

    Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning

    Xiangde Luo, Guotai Wang, Tao Song, Jingyang Zhang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, and Shaoting Zhang. Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning. Medical image analysis , 72: 1021...

    2021

  78. [86]

    Scribble- supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision

    Xiangde Luo, Minhao Hu, Wenjun Liao, Shuwei Zhai, Tao Song, Guotai Wang, and Shaoting Zhang. Scribble- supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision. In International Conference on Medical Image Comput- ing and Co...

    2022

  79. [87]

    Fast and low-gpu-memory abdomen ct organ seg- mentation: the flare challenge

    Jun Ma, Yao Zhang, Song Gu, Xingle An, Zhihe Wang, Cheng Ge, Congcong Wang, Fan Zhang, Yu Wang, Yinan Xu, et al. Fast and low-gpu-memory abdomen ct organ seg- mentation: the flare challenge. Medical Image Analysis, 82: 102616, 2022. 19

    2022

  80. [88]

    Segment anything in medical images

    Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medical images. Nature Communications, 15:1–9, 2024. 1, 2, 5, 22 12

    2024

  81. [89]

    Rose: a retinal oct-angiography vessel segmentation dataset and new model

    Yuhui Ma, Huaying Hao, Jianyang Xie, Huazhu Fu, Jiong Zhang, Jianlong Yang, Zhen Wang, Jiang Liu, Yalin Zheng, and Yitian Zhao. Rose: a retinal oct-angiography vessel segmentation dataset and new model. IEEE Transactions on Medical Imaging, 40(3):928–939, 2021. 4, 19

    2021

  82. [90]

    Macdonald, Zhe Zhu, Brandon Konkel, Maciej Mazurowski, Walter Wiggins, and Mustafa Bashir

    Jacob A. Macdonald, Zhe Zhu, Brandon Konkel, Maciej Mazurowski, Walter Wiggins, and Mustafa Bashir. Duke liver dataset (MRI) v2, 2023. 4, 19

    2023

  83. [91]

    Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults

    Daniel S Marcus, Tracy H Wang, Jamie Parker, John G Csernansky, John C Morris, and Randy L Buckner. Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience , 19(9):...

    2007

  84. [92]

    The parkinson progression marker initiative (ppmi)

    Kenneth Marek, Danna Jennings, Shirley Lasch, Andrew Siderowf, Caroline Tanner, Tanya Simuni, Chris Coffey, Karl Kieburtz, Emily Flagg, Sohini Chowdhury, et al. The parkinson progression marker initiative (ppmi). Progress in neurobiology, 95(4):629–635, 2011. 4, 19

    2011

  85. [93]

    Meiburger

    Francesco Marzola, Nens Van Alfen, Jonne Doorduin, and Kristen M. Meiburger. Deep learning segmentation of transverse musculoskeletal ultrasound images for neuro- muscular disease assessment. Computers in Biology and Medicine, 135:104623, 2021. 19

    2021

  86. [94]

    Maciej A Mazurowski, Kal Clark, Nicholas M Czarnek, Parisa Shamsesfandabadi, Katherine B Peters, and Ashir- bani Saha. Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tu- mor genomic subtypes and patient outcomes in a multi- institu...

    2017

  87. [95]

    Quantification of uncertainties in biomed- ical image quantification 2021

    Bjoern Menze, Leo Joskowicz, Spyridon Bakas, Andras Jakab, Ender Konukoglu, Anton Becker, Amber Simpson, and Richard D. Quantification of uncertainties in biomed- ical image quantification 2021. 4th International Confer- ence on Medical Image Computing and Computer Assisted In...

    2021

  88. [96]

    The multimodal brain tumor image segmentation benchmark (brats)

    Bjoern H Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging, 34(1...

    1993

  89. [97]

    Ultrasound nerve segmentation, 2016

    Anna Montoya, Hasnin, kaggle446, shirzad, Will Cukier- ski, and yffud. Ultrasound nerve segmentation, 2016. 4, 19

    2016

  90. [98]

    Adap- tivesam: Towards efficient tuning of sam for surgical scene segmentation

    Jay N Paranjape, Nithin Gopalakrishnan Nair, Shameema Sikder, S Swaroop Vedula, and Vishal M Patel. Adap- tivesam: Towards efficient tuning of sam for surgical scene segmentation. In Annual Conference on Medical Image Un- derstanding and Analysis, pages 187–201. Springer, 2024. 2

    2024

  91. [99]

    An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset

    Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, et al. An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Sc...

    2021

  92. [100]

    An open access thy- roid ultrasound image database

    Lina Pedraza, Carlos Vargas, Fabi´an Narv´aez, Oscar Dur´an, Emma Mu˜noz, and Eduardo Romero. An open access thy- roid ultrasound image database. In 10th international sym- posium on medical information processing and analysis , page 92870W. SPIE / International Society for Op...

    2015

  93. [101]

    HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset

    Ga ˇsper Podobnik, Primo ˇz Strojan, Primo ˇz Peterlin, Bulat Ibragimov, and Toma ˇz Vrtovec. HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset. Medical Physics , 50(3):1917–1927, 2023. tex.eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.1...

    1917 doi

  94. [102]

    Indian diabetic retinopathy image dataset (idrid), 2018

    Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, and Fabrice Meriaudeau. Indian diabetic retinopathy image dataset (idrid), 2018. 4, 19

    2018

  95. [103]

    Evaluation framework for algorithms segmenting short axis cardiac mri

    Perry Radau, Yingli Lu, Kim Connelly, Gideon Paul, AJWG Dick, and Graham Wright. Evaluation framework for algorithms segmenting short axis cardiac mri. The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge, 49, 2009. 4, 5, 17, 18

    2009

  96. [104]

    Wong, Jose Javier Gonzalez Or- tiz, Beth Cimini, John V

    Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Or- tiz, Beth Cimini, John V . Guttag, and Adrian V . Dalca. Tyche: Stochastic in-context learning for medical image segmentation. Computer Vision and Pattern Reconition (CVPR), 2024. 2, 4, 16, 17

    2024

  97. [105]

    Uncle sam: Unleash- ing sam’s potential for continual prostate mri segmentation

    Amin Ranem, Mohamed Afham Mohamed Aflal, Moritz Fuchs, and Anirban Mukhopadhyay. Uncle sam: Unleash- ing sam’s potential for continual prostate mri segmentation. In Medical Imaging with Deep Learning, 2024. 2

    2024

  98. [106]

    Sam 2: Segment anything in images and videos

    Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Ro- man R ¨adle, Chloe Rolland, Laura Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714, 2024. 2

    2024 arXiv

  99. [107]

    Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L. Rubin. CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Scientific Data, 7(1):381, 2020. 4, 19

    2020

  100. [108]

    Rotkopf, Heinz-Peter Schlemmer, and Klaus Maier-Hein

    Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal, Balint Kovacs, Saikat Roy, Constantin Ulrich, Tassilo Wald, Lukas T. Rotkopf, Heinz-Peter Schlemmer, and Klaus Maier-Hein. Lesionlocator: Zero-shot universal tumor seg- mentation and tracking in 3d whole-body imaging. 2025. 5

    2025

  101. [109]

    U- net: Convolutional networks for biomedical image seg- mentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image seg- mentation. In Medical Image Computing and Computer- Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Pro- ceedings,...

    2015

  102. [110]

    Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, and Daguang Xu

    Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, and Daguang Xu. Going to Extremes: Weakly Super- vised Medical Image Segmentation. Machine Learning and Knowledge Extraction, 3(2):507–524, 2021. 2

    2021

  103. [111]

    Erickson

    Tomas Sakinis, Fausto Milletari, Holger Roth, Panagio- tis Korfiatis, Petro Kostandy, Kenneth Philbrick, Zeynettin 13 Akkus, Ziyue Xu, Daguang Xu, and Bradley J. Erickson. Interactive segmentation of medical images through fully convolutional neural networks, 2019. arXiv:1903.08205. 2

    2019 arXiv

  104. [112]

    Deep learn- ing saliency maps do not accurately highlight diagnostically relevant regions for medical image interpretation.MedRxiv,

    Adriel Saporta, Xiaotong Gui, Ashwin Agrawal, Anuj Pa- reek, SQ Truong, CD Nguyen, Van-Doan Ngo, Jayne Seekins, Francis G Blankenberg, AY Ng, et al. Deep learn- ing saliency maps do not accurately highlight diagnostically relevant regions for medical image interpretation.MedRxiv,

  105. [113]

    Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H

    Constantin Seibold, Simon Reiß, Saquib Sarfraz, Matthias A. Fink, Victoria Mayer, Jan Sellner, Moon Sung Kim, Klaus H. Maier-Hein, Jens Kleesiek, and Rainer Stiefelhagen. Detailed annotations of chest x-rays via ct projection for report understanding. In Proceedings of the 33t...

    2022

  106. [114]

    Construction of a consistent high-definition spatio-temporal atlas of the de- veloping brain using adaptive kernel regression

    Ahmed Serag, Paul Aljabar, Gareth Ball, Serena J Counsell, James P Boardman, Mary A Rutherford, A David Edwards, Joseph V Hajnal, and Daniel Rueckert. Construction of a consistent high-definition spatio-temporal atlas of the de- veloping brain using adaptive kernel regression....

    2012

  107. [115]

    Validation, comparison, and combi- nation of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 chal- lenge

    Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas De Bel, Moira SN Berens, Cas Van Den Bogaard, Piergior- gio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, et al. Validation, comparison, and combi- nation of algorithms for automatic detection of pulmonar...

    2017

  108. [116]

    A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L Simpson, Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram Van Ginneken, An- nette Kopp-Schneider, Bennett A Landman, Geert Litjens, Bjoern Menze, et al. A large annotated medical image dataset for the development and evaluation of segmentation a...

    1902 arXiv

  109. [117]

    F-BRS: Rethinking Backpropagating Re- finement for Interactive Segmentation

    Konstantin Sofiiuk, Ilia Petrov, Olga Barinova, and An- ton Konushin. F-BRS: Rethinking Backpropagating Re- finement for Interactive Segmentation. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8620–8629, Seattle, W A, USA, 2020. IEEE. 2

    2020

  110. [118]

    Petrov, and Anton Konushin

    Konstantin Sofiiuk, Ilia A. Petrov, and Anton Konushin. Reviving Iterative Training with Mask Guidance for Inter- active Segmentation, 2021. arXiv:2102.06583 [cs]. 5

    2021 arXiv

  111. [119]

    CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthe- sized data

    Yuxin Song, Jing Zheng, Long Lei, Zhipeng Ni, Baoliang Zhao, and Ying Hu. CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthe- sized data. Ultrasonics, 122:106706, 2022. 4, 19

    2022

  112. [120]

    Ridge-based vessel segmentation in color images of the retina

    Joes Staal, Michael D Abr `amoff, Meindert Niemeijer, Max A Viergever, and Bram Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4):501–509, 2004. 4, 18

    2004

  113. [121]

    Boxinst: High-performance instance segmentation with box annotations

    Zhi Tian, Chunhua Shen, Xinlong Wang, and Hao Chen. Boxinst: High-performance instance segmentation with box annotations. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition , pages 5443–5452, 2021. 2

    2021

  114. [122]

    Stegmann, and Marco Loog

    Bram van Ginneken, Mikkel B. Stegmann, and Marco Loog. Segmentation of anatomical structures in chest ra- diographs using supervised methods: a comparative study on a public database. Medical Image Analysis , 10(1):19– 40, 2006. 4, 5, 6, 7, 18, 20

    2006

  115. [123]

    Improving realism in patient- specific abdominal ultrasound simulation using cyclegans

    Santiago Vitale, Jos ´e Ignacio Orlando, Emmanuel Iarussi, and Ignacio Larrabide. Improving realism in patient- specific abdominal ultrasound simulation using cyclegans. International journal of computer assisted radiology and surgery, 15(2):183–192, 2020. 19

    2020

  116. [124]

    Sam-octa: A fine-tuning strategy for applying foun- dation model to octa image segmentation tasks, 2023

    Chengliang Wang, Xinrun Chen, Haojian Ning, and Shiy- ing Li. Sam-octa: A fine-tuning strategy for applying foun- dation model to octa image segmentation tasks, 2023. 2

    2023

  117. [125]

    Dy- namically Balanced Online Random Forests for Interactive Scribble-Based Segmentation

    Guotai Wang, Maria A Zuluaga, Rosalind Pratt, Michael Aertsen, Tom Doel, Maria Klusmann, Anna L David, Jan Deprest, Tom Vercauteren, and Sebastien Ourselin. Dy- namically Balanced Online Random Forests for Interactive Scribble-Based Segmentation. In Medical Image Comput- ing a...

    2016

  118. [126]

    Zuluaga, Rosalind Pratt, Premal A

    Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, and Tom Ver- cauteren. Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning. IEEE Transact...

    2018

  119. [127]

    Deepigeos: a deep interactive geodesic framework for medical image segmen- tation

    Guotai Wang, Maria A Zuluaga, Wenqi Li, Rosalind Pratt, Premal A Patel, Michael Aertsen, Tom Doel, Anna L David, Jan Deprest, S ´ebastien Ourselin, et al. Deepigeos: a deep interactive geodesic framework for medical image segmen- tation. IEEE transactions on pattern analysis a...

    2018

  120. [128]

    Seggpt: Towards seg- menting everything in context

    Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, and Tiejun Huang. Seggpt: Towards seg- menting everything in context. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 1130–1140, 2023. 2

    2023

  121. [129]

    Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images

    Jakob Wasserthal, Hanns-Christian Breit, Manfred T Meyer, Maurice Pradella, Daniel Hinck, Alexander W Sauter, Tobias Heye, Daniel T Boll, Joshy Cyriac, Shan Yang, et al. Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial In- te...

    2023

  122. [130]

    Wong, Marianne Rakic, John Guttag, and Adrian V

    Hallee E. Wong, Marianne Rakic, John Guttag, and Adrian V . Dalca. Scribbleprompt: Fast and flexible interac- tive segmentation for any medical image. European Con- ference on Computer Vision (ECCV), 2024. 1, 2, 3, 4, 5, 16, 17, 22

    2024

  123. [131]

    Efficient in-context medical seg- mentation with meta-driven visual prompt selection

    Chenwei Wu, David Restrepo, Zitao Shuai, Zhongming Liu, and Liyue Shen. Efficient in-context medical seg- mentation with meta-driven visual prompt selection. In In- ternational Conference on Medical Image Computing and Computer-Assisted Intervention, pages 255–265. Springer,

  124. [132]

    One-prompt to segment all med- ical images

    Junde Wu and Min Xu. One-prompt to segment all med- ical images. In Computer Vision and Pattern Recognition (CVPR), pages 11302–11312, 2024. 2, 5, 17, 22 14

    2024

  125. [133]

    Medical sam adapter: Adapt- ing segment anything model for medical image segmenta- tion

    Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin. Medical sam adapter: Adapt- ing segment anything model for medical image segmenta- tion. arXiv preprint arXiv:2304.12620, 2023. 2

    2023 arXiv

  126. [134]

    Deep Interactive Object Selection

    Ning Xu, Brian Price, Scott Cohen, Jimei Yang, and Thomas Huang. Deep Interactive Object Selection. In 2016 IEEE Conference on Computer Vision and Pattern Recog- nition (CVPR), pages 373–381, Las Vegas, NV , USA, 2016. IEEE. 5

    2016

  127. [135]

    Deep GrabCut for Object Selection

    Ning Xu, Brian Price, Scott Cohen, Jimei Yang, and Thomas Huang. Deep GrabCut for Object Selection. arXiv,

  128. [136]

    Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks

    Jin Ye, Junlong Cheng, Jianpin Chen, Zhongying Deng, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, et al. Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks. arXiv preprint arXiv:2311.11969, 2023. 2

    2023 arXiv

  129. [137]

    User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability

    Paul A Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C Gee, and Guido Gerig. User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage, 31(3):1116–1128, 2006. 1

    2006

  130. [138]

    Customized segment any- thing model for medical image segmentation, 2023

    Kaidong Zhang and Dong Liu. Customized segment any- thing model for medical image segmentation, 2023. 2

    2023

  131. [139]

    Cyclemix: A holistic strat- egy for medical image segmentation from scribble supervi- sion

    Ke Zhang and Xiahai Zhuang. Cyclemix: A holistic strat- egy for medical image segmentation from scribble supervi- sion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 11656–11665,

  132. [140]

    Interactive Object Segmentation With Inside-Outside Guidance

    Shiyin Zhang, Jun Hao Liew, Yunchao Wei, Shikui Wei, and Yao Zhao. Interactive Object Segmentation With Inside-Outside Guidance. In 2020 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 12231–12241. IEEE, 2020. 2

    2020

  133. [141]

    Busis: A benchmark for breast ultrasound image segmentation

    Yingtao Zhang, Min Xian, Heng-Da Cheng, Bryar Shareef, Jianrui Ding, Fei Xu, Kuan Huang, Boyu Zhang, Chun- ping Ning, and Ying Wang. Busis: A benchmark for breast ultrasound image segmentation. In Healthcare, page 729. MDPI, 2022. 4, 19

    2022

  134. [142]

    Guttag, and Adrian V

    Amy Zhao, Guha Balakrishnan, Fredo Durand, John V . Guttag, and Adrian V . Dalca. Data augmentation using learned transformations for one-shot medical image seg- mentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 5, 17

    2019

  135. [143]

    A multi-modality ovarian tumor ultrasound image dataset for unsupervised cross-domain semantic segmenta- tion

    Qi Zhao, Shuchang Lyu, Wenpei Bai, Linghan Cai, Bing- hao Liu, Meijing Wu, Xiubo Sang, Min Yang, and Lijiang Chen. A multi-modality ovarian tumor ultrasound image dataset for unsupervised cross-domain semantic segmenta- tion. CoRR, abs/2207.06799, 2022. 4, 19

    2022 arXiv

  136. [144]

    A continual learning framework for uncertainty- aware interactive image segmentation

    Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, and Anne Haake. A continual learning framework for uncertainty- aware interactive image segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6030– 6038, 2021. 2

    2021

  137. [145]

    Evaluation and comparison of 3d intervertebral disc localization and segmentation methods for 3d t2 mr data: A grand challenge

    Guoyan Zheng, Chengwen Chu, Daniel L Belav `y, Bu- lat Ibragimov, Robert Korez, Toma ˇz Vrtovec, Hugo Hutt, Richard Everson, Judith Meakin, Isabel L ˘opez Andrade, et al. Evaluation and comparison of 3d intervertebral disc localization and segmentation methods for 3d t2 mr dat...

  138. [146]

    train” splits from the 67 training datasets to train Multi- verSeg models. We use the “validation

    Xin Zheng, Yong Wang, Guoyou Wang, and Jianguo Liu. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron, 107:55–71, 2018. 4, 5, 6, 7, 18, 20, 22, 26 15 MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In...

    2018

  139. [2017]

    arXiv:1707.00243 [cs]. 2

    arXiv

  140. [2023]

    2, 3, 4, 5, 8, 16, 17, 21, 22

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.