Pith. sign in

REVIEW 3 major objections 6 minor 45 references

EchoONE: Segmenting Multiple echocardiography Planes in One Model

T0 review · 3 major / 6 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 The paper claims that a single SAM-based network with two new modules—PC-Mask for dense prompts and LFFA for feature fusion—can segment the left ventricle, left atrium, and myocardium across four echocardiography views in one model…

desk verdict EchoONE is a solid engineering contribution to multi-plane echocardiography segmentation, but its headline generalization claim leans on a dataset whose LV ground truth is algorithmically generated. read the letter →

arxiv 2412.02993 v3 pith:N54NNXFG submitted 2024-12-04 cs.CV

Jiongtong Hu , Wei Zhuo , Jun Cheng , Yingying Liu , Wufeng Xue , Dong Ni This is my paper · ORCID
classification cs.CV
keywords echocardiographysegmentationmulti-planeSegmentAnythingModeldensepromptgenerationSAMadaptationcardiacultrasoundleftventriclemedicalimage
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

The paper aims to solve the multi-plane segmentation problem for echocardiography: instead of training one network per heart view (2CH, 3CH, 4CH, PSAX), it proposes a single uniform model called EchoONE. The model builds on Segment Anything (SAM), adds a module that generates semantic-aware dense prompts without knowing which plane the input comes from, and a module that fuses local CNN features into SAM's decoder. On internal and external datasets, EchoONE reports consistently higher Dice scores than plane-specific U-Nets and adapted SAM variants across all structures and planes. If true, this would let a clinic deploy one segmentation model for a full echo exam rather than a collection of view-specific models.

What carries the argument

PC-Mask (prior-composable mask learning): a module that clusters images in a pre-trained feature space, stores a center mask per cluster, and for each input computes cosine similarity to cluster prototypes, then builds a dense prompt as a weighted concatenation of the center masks; a light U-Net turns this prior embedding into a mask prompt for SAM. LFFA (local feature fusion and adaption): a set of 1x1 convolutions that concatenate CNN branch features with the transformer block outputs of SAM's mask decoder, injecting local boundary detail and accelerating convergence.

What would settle it

On a dataset with manual LV cavity annotations (e.g., CAMUS, which provides LV labels), recompute EchoONE's LV Dice using the manual masks instead of the filled masks. If EchoONE no longer matches the reported numbers or no longer beats SAMUS on LV, the central state-of-the-art claim for LV segmentation collapses.

Watch

Extended reading notes

Core claim

The central discovery is that the multi-plane segmentation problem in echocardiography can be solved by one model if the segmentation prompt is made plane-aware without using plane labels. EchoONE achieves this with PC-Mask, which groups training images by visual similarity, learns a prototype mask per group, and composes a per-image dense prompt from a weighted combination of these prototypes. A CNN branch with LFFA adapts SAM's encoder and decoder to ultrasound data. The paper reports that EchoONE outperforms both conventional segmentation networks (U-Net, DeepLabV3+, SwinUNet, H2Former) and six SAM-based methods on all four planes, all cardiac structures, and all internal and external datasets tested.

Load-bearing premise

The paper generates synthetic left-ventricle cavity labels from myocardium annotations by detecting landmarks and filling the cavity, and it uses these filled masks as ground truth for both training and evaluation; if this filling is biased, the reported LV Dice improvements do not reflect true segmentation quality.

Editorial extensions

If this is right

  • A single trained EchoONE model can be deployed for a standard transthoracic echo exam covering 2CH, 3CH, 4CH, and PSAX views, removing the need for a per-view model zoo.
  • Because the dense prompt is constructed without plane labels, EchoONE can be applied to unseen views whose plane type is unknown at inference time.
  • The architecture is modally agnostic; the paper argues it should transfer to multi-plane segmentation in other medical imaging modalities.
  • The LFFA convergence speedup could reduce training cost for SAM-based medical segmentation generally.

Reading between the lines

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

  • If the synthetic LV filling is biased, the reported LV margins over SAMUS may shrink once manual LV masks are used; a re-evaluation with true LV annotations would settle this.
  • PC-Mask's cluster prototypes act as a soft, unsupervised plane classifier; one could test whether the number of clusters K matters or whether the method degrades when plane-specific appearance shifts (e.g., new ultrasound machines) are not represented in the clusters.
  • EchoONE could be extended to video (e.g., beat-to-beat assessment) by treating frames as an additional plane-like dimension; the paper does not explore temporal consistency.
  • For clinical deployment, the model's HD95 on PSAX is the largest gap; boundary quality on short-axis views may be the limiting factor even if Dice is high.
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

3 major / 6 minor

Summary. EchoONE proposes a SAM-based segmentation architecture with two new components: PC-Mask, which generates dense mask prompts by linearly combining cluster prototypes in a ResNet34 latent space without explicit plane labels at inference, and LFFA, a learnable CNN branch with cross-branch attention that fuses local features into the mask decoder. The authors unify heterogeneous annotation protocols, including filling LV cavities from myocardium-only annotations, and train one model on four plane categories from five datasets. They report plane-wise, structure-wise, dataset-wise, and external results showing consistent gains over U-Net, DeepLabV3+, SwinUNet, H2Former, and six SAM-based competitors, with ablations attributing the gains to PC-Mask and LFFA.

Significance. The practical goal is worthwhile, and the evaluation breadth across five internal and two external datasets is a strength. If the results are reliable, EchoONE would be a useful single-model alternative to plane-specific echocardiography segmentation pipelines, and the PC-Mask idea of composing dense prompts from prior prototypes is a transferable contribution. The manuscript does not yet provide code or checkpoints, and the 'first uniform model' claim is stated more strongly than the related-work discussion supports. The central results are plausible, but the external evidence is compromised by the label-generation issue described below, and no uncertainty quantification is provided for the small margins over SAMUS.

major comments (3)
  1. [§4.1, Table 1, Table 4] Table 1 shows that HMCQU carries only MYO annotations, and §4.1 states that for images with only MYO annotations the LV cavity mask is generated by detecting landmarks and filling the cavity. The HMCQU row of Table 4 is then used in §4.3.2 as evidence of external generalization ('our method still delivers a Dice score of 73.94%'). Because the LV component of that score is computed against algorithmically filled masks rather than expert LV contours, the reported score does not establish segmentation accuracy for the LV in HMCQU. This is load-bearing because HMCQU is one of only two external datasets supporting the generalization claim. The authors should either validate the generated labels against manual LV contours, report HMCQU results restricted to structures with native annotations (MYO), or clearly mark the LV result as a proxy. The description of the landmark-detection and filling procedure is also too brief to assess potential bias.
  2. [Tables 2-4] The headline claim is 'consistently state-of-the-art', but all reported numbers are single-run point estimates. The margins over SAMUS are, for example, +2.16, +1.73, +1.91, and +2.34 mDice for 2CH, 3CH, 4CH, and PSAX in Table 2, and +0.80 mDice on EchoNet-Dynamic in Table 4. With no error bars, no repeated runs, and no paired significance tests, the reader cannot tell whether these differences are systematic or noise. Reporting per-subject confidence intervals or paired tests across the five internal datasets would materially strengthen the central claim.
  3. [§3.2, Figure 3] The authors emphasize that PC-Mask 'does not know the plane information' and that this property makes EchoONE a uniform model. However, the latent space used for prototypes is obtained from a ResNet34 pretrained for plane classification, so the prompt generation is built on plane-discriminative features, and plane labels are used during that pretraining. The paper should clarify exactly how the classifier is trained, whether plane labels are used, and whether the 'without plane information' claim refers only to inference. If it refers only to inference, the novelty statements in the Abstract and Introduction should be adjusted accordingly.
minor comments (6)
  1. [§4.1] The unified label definition is inconsistent: the text says '1 for LV, 2 for LV cavity, and 3 for MYO', while Figure 5(a) and the annotation list say '1 LA, 2 LV, 3 MYO'. Please correct the numbering.
  2. [Figure 1] The mDice values printed under the four panels do not match Table 2; for example, the line that should correspond to EchoONE shows 25.10, 27.22, 35.30, and 26.24, which are SAM's values in Table 2. The figure and caption need to be re-checked.
  3. [Abstract and §4.1] The paper claims six echocardiographic planes, but the experiments merge PSAX-B, PSAX-M, and PSAX-A into a single PSAX category; please state clearly whether the reported results are for four plane groups or six individual planes.
  4. [§4.3.2] The dataset name 'HMC QU' appears with a space; use one consistent name throughout the text and tables.
  5. [§4.4] The last sentence reads 'extra benefits four segmentation' and should be 'for segmentation'; the sentence beginning 'it can be drawn that By leveraging' should be rephrased.
  6. [§5] The conclusion's claim that the method generalizes to MPS problems in other medical image modalities is speculative; either add supporting evidence or soften the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; the generated-LV-label issue is a ground-truth validity caveat, not a reduction of the claimed result to its inputs.

full rationale

EchoONE is trained and evaluated as a standard supervised segmentation system: the PC-Mask prototypes and center masks are computed from training data, and the final SAM decoder output and the PC-Mask dense prompt are both supervised by the same held-out-subject labels. That is conventional coarse-to-fine supervision, not a circular reduction: neither the dense prompt nor the final prediction is defined as the ground-truth label, and the evaluation subjects are disjoint from training subjects. The only unusual step is in Section 4.1, where images annotated only with MYO are given LV cavity masks by 'detecting the landmarks and filling the cavity', and these generated masks are then used both as training targets and as the reference for external HMCQU evaluation in Table 4. This is a real measurement-validity concern for the reported HMCQU LV Dice, especially because the paper itself describes HMCQU as having 'annotations are noisy'. However, it is not a circularity in the derivation chain: the landmark-detection-and-filling routine is not performed by EchoONE, is not a function of EchoONE's output, and is not a fitted parameter renamed as a prediction. The ablation study of PC-Mask and LFFA is an independent internal check, and the comparisons to U-Net, DeepLabV3+, SwinUNet, H2Former, MedSAM, SAMed, SAM-Med2D, MSA, and SAMUS are against external prior work rather than a self-citation loop. Overall, the central claim does not reduce by construction to its inputs, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim rests primarily on three assumptions: the validity of algorithmically generated LV cavity labels, the semantic usefulness of the pretrained feature space for prototype composition, and the decision to collapse three PSAX levels into one plane. No new physical entities, particles, or forces are postulated; PC-Mask and LFFA are model components, not invented entities.

free parameters (3)
  • Number of PC-Mask clusters K
    The PC-Mask module groups images into K clusters; K is not specified in the text, yet it determines the number of prototypes and center masks, directly shaping the dense prompts.
  • PC-Mask loss weight lambda = 0.5
    Equation (6) sets lambda=0.5 to trade off segmentation loss and PC-Mask loss; chosen by hand without justification.
  • Loss weights for dice and BCE = 0.8 and 0.2
    Section 3.4: L_SEG combines dice loss and BCE with weights 0.8 and 0.2; these are chosen without justification.
assumptions (3)
  • ad hoc to paper The landmark-filling procedure generates valid LV cavity ground truth from MYO annotations.
    Section 4.1 and Figure 5(b): for images with only MYO annotation, the LV cavity is produced by detecting landmarks and filling the cavity; this synthetic label is used as ground truth in training and evaluation without independent validation against manually traced LV cavities.
  • domain assumption A pretrained ResNet34 latent space provides semantically meaningful similarity for composing plane-specific prototype masks.
    Section 3.2: cosine similarity in the ResNet34 feature space is used to weight and compose center masks; the paper does not verify that this space separates the echocardiographic planes or that weighted averages of masks are useful prompts.
  • domain assumption The three parasternal short-axis levels (basal, mid-ventricle, apical) can be treated as a single PSAX plane.
    Section 4.1: 'Given the fact that the three PSAX planes share similarities, we treat them as one PSAX plane in this work.' This collapses potentially distinct anatomical planes and affects both training targets and evaluation grouping.

how reviews work

0 comments
Cite this review

Pith. "Pith review of EchoONE: Segmenting Multiple echocardiography Planes in One Model." pith.science (2026). https://pith.science/paper/N54NNXFG

@misc{pith2026241202993,
  author       = {Pith},
  title        = {Pith review of: EchoONE: Segmenting Multiple echocardiography Planes in One Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N54NNXFG}},
  note         = {Machine review of arXiv:2412.02993}
}
read the original abstract

In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment of cardiac disease. AI models for echocardiography have to be tailored for each specific plane due to the dramatic structure differences, thus resulting in repetition development and extra complexity. Effective solution for such a multi-plane segmentation (MPS) problem is highly demanded for medical images, yet has not been well investigated. In this paper, we propose a novel solution, EchoONE, for this problem with a SAM-based segmentation architecture, a prior-composable mask learning (PC-Mask) module for semantic-aware dense prompt generation, and a learnable CNN-branch with a simple yet effective local feature fusion and adaption (LFFA) module for SAM adapting. We extensively evaluated our method on multiple internal and external echocardiography datasets, and achieved consistently state-of-the-art performance for multi-source datasets with different heart planes. This is the first time that the MPS problem is solved in one model for echocardiography data. The code will be available at https://github.com/a2502503/EchoONE.

Figures

Figures reproduced from arXiv: 2412.02993 by the authors.

Figure 1
Figure 1. Comparison of existing solutions for the Multi-plane [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of our EchoONE, which contains a SAM-based segmentation architecture (middle), a prior-composable mask learning [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Local feature fusion and adaption (LFFA). The output [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: (a) Remapping annotations to unified mask representa [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: Performance comparison of EchoONE and its competi [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Visualization of the segmentation results by EchoONE and existing methods. The green, blue, and red lines correspond to the [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 33 canonical work pages

  1. [1]

    Swin-unet: Unet-like pure transformer for medical image segmentation

    Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xi- aopeng Zhang, Qi Tian, and Manning Wang. Swin-unet: Unet-like pure transformer for medical image segmentation. In ECCV, pages 205–218. Springer, 2022. 6, 7, 8

    work page 2022

  2. [2]

    Ladder fine-tuning approach for sam integrating complementary net- work

    Shurong Chai, Rahul Kumar Jain, Shiyu Teng, Jiaqing Liu, Yinhao Li, Tomoko Tateyama, and Yen-wei Chen. Ladder fine-tuning approach for sam integrating complementary net- work. arXiv preprint arXiv:2306.12737, 2023. 3, 4

    arXiv 2023

  3. [3]

    Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation

    Cheng Chen, Juzheng Miao, Dufan Wu, Aoxiao Zhong, Zhiling Yan, Sekeun Kim, Jiang Hu, Zhengliang Liu, Lichao Sun, Xiang Li, et al. Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation. Medical Image Analysis, 98:103310, 2024. 2

    work page 2024

  4. [4]

    The ability of segmenting anything model (sam) to segment ultrasound images

    Fang Chen, Lingyu Chen, Haojie Han, Sainan Zhang, Dao- qiang Zhang, and Hongen Liao. The ability of segmenting anything model (sam) to segment ultrasound images. Bio- Science Trends, 17(3):211–218, 2023. 2

    work page 2023

  5. [5]

    Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model

    Keyan Chen, Chenyang Liu, Hao Chen, Haotian Zhang, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi. Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model. IEEE Transactions on Geoscience and Remote Sensing, 2024. 3

    2024

  6. [6]

    Encoder-decoder with atrous separable convolution for semantic image segmentation

    Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, pages 801–818, 2018. 6, 7, 8

    work page 2018

  7. [7]

    Sam-med2d

    Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Ji- long Chen, Lei Jiang, et al. Sam-med2d. arXiv preprint arXiv:2308.16184, 2023. 3, 6, 7, 8

    arXiv 2023

  8. [8]

    Samaug: Point prompt augmentation for segment anything model

    Haixing Dai, Chong Ma, Zhiling Yan, Zhengliang Liu, Enze Shi, Yiwei Li, Peng Shu, Xiaozheng Wei, Lin Zhao, Zihao Wu, et al. Samaug: Point prompt augmentation for segment anything model. arXiv preprint arXiv:2307.01187, 2023. 3

    arXiv 2023

Show all 45 references
  1. [9]

    Mvfusfra: A multi-view dynamic fusion framework for multimodal brain tumor segmentation

    Yi Ding, Wei Zheng, Ji Geng, Zhen Qin, Kim-Kwang Ray- mond Choo, Zhiguang Qin, and Xiaolin Hou. Mvfusfra: A multi-view dynamic fusion framework for multimodal brain tumor segmentation. IEEE Journal of Biomedical and Health Informatics, 26(4):1570–1581, 2021. 2

    2021

  2. [10]

    Gvcnn: Group-view convolutional neural networks for 3d shape recognition

    Yifan Feng, Zizhao Zhang, Xibin Zhao, Rongrong Ji, and Yue Gao. Gvcnn: Group-view convolutional neural networks for 3d shape recognition. In CVPR, pages 264–272, 2018. 1

    2018

  3. [11]

    H2former: An efficient hierarchical hybrid transformer for medical image segmentation

    Along He, Kai Wang, Tao Li, Chengkun Du, Shuang Xia, and Huazhu Fu. H2former: An efficient hierarchical hybrid transformer for medical image segmentation. IEEE Trans- actions on Medical Imaging , 42(9):2763–2775, 2023. 6, 7, 8

    2023

  4. [12]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. 4

    2016

  5. [13]

    Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects

    Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects. In AAAI, pages 12511–12518, 2024. 2

    2024

  6. [14]

    Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

    Yuhao Huang, Xin Yang, Lian Liu, Han Zhou, Ao Chang, Xinrui Zhou, Rusi Chen, Junxuan Yu, Jiongquan Chen, Chaoyu Chen, et al. Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024. 2, 3

    2024

  7. [15]

    Multi-view sa-la net: A framework for simultaneous seg- mentation of rv on multi-view cardiac mr images

    Sana Jabbar, Syed Talha Bukhari, and Hassan Mohy-ud Din. Multi-view sa-la net: A framework for simultaneous seg- mentation of rv on multi-view cardiac mr images. In Inter- national Workshop on Statistical Atlases and Computational Models of the Heart, pages 277–286. Springer, 2021. 2

    2021

  8. [16]

    Left ventricular wall motion estimation by active 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 active polynomials for acute myocardial infarction detection. IEEE A...

    2020

  9. [17]

    Segment any- thing

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer White- head, Alexander C Berg, Wan-Yen Lo, et al. Segment any- thing. In ICCV, pages 4015–4026, 2023. 2, 6, 7, 8

    2023

  10. [18]

    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

  11. [19]

    Promise: Prompt-driven 3d medical image segmentation us- ing pretrained image foundation models

    Hao Li, Han Liu, Dewei Hu, Jiacheng Wang, and Ipek Oguz. Promise: Prompt-driven 3d medical image segmentation us- ing pretrained image foundation models. In 2024 IEEE In- ternational Symposium on Biomedical Imaging (ISBI), pages 1–5. IEEE, 2024. 3

    2024

  12. [20]

    Right ventricular segmentation from short-and long-axis mris via information transition

    Lei Li, Wangbin Ding, Liqin Huang, and Xiahai Zhuang. Right ventricular segmentation from short-and long-axis mris via information transition. In International Work- shop on Statistical Atlases and Computational Models of the Heart, pages 259–267. Springer, 2021. 2

    2021

  13. [21]

    Samus: Adapting segment anything model for clinically-friendly and generalizable ultrasound image segmentation

    Xian Lin, Yangyang Xiang, Li Zhang, Xin Yang, Zengqiang Yan, and Li Yu. Samus: Adapting segment anything model for clinically-friendly and generalizable ultrasound image segmentation. arXiv preprint arXiv:2309.06824 , 2023. 3, 4, 6, 7, 8

    2023 arXiv

  14. [22]

    Transfusion: multi-view divergent fusion for medical image segmentation with transformers

    Di Liu, Yunhe Gao, Qilong Zhangli, Ligong Han, Xiaoxiao He, Zhaoyang Xia, Song Wen, Qi Chang, Zhennan Yan, Mu Zhou, et al. Transfusion: multi-view divergent fusion for medical image segmentation with transformers. In MICCAI, pages 485–495. Springer, 2022. 2

    2022

  15. [23]

    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):654, 2024. 3, 6, 7, 8

    2024

  16. [24]

    Deep learning segmentation of the right ventricle in cardiac mri: the m&ms challenge

    Carlos Mart ´ın-Isla, V´ıctor M Campello, Cristian Izquierdo, Kaisar Kushibar, Carla Sendra-Balcells, Polyxeni Gkontra, Alireza Sojoudi, Mitchell J Fulton, Tewodros Weldebirhan Arega, Kumaradevan Punithakumar, et al. Deep learning segmentation of the right ventricle in cardiac...

    2023

  17. [25]

    Segment anything model for medical image analysis: an experimental study

    Maciej A Mazurowski, Haoyu Dong, Hanxue Gu, Jichen Yang, Nicholas Konz, and Yixin Zhang. Segment anything model for medical image analysis: an experimental study. Medical Image Analysis, 89:102918, 2023. 2

    2023

  18. [26]

    V-net: Fully convolutional neural networks for volumetric medical image segmentation

    Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV), pages 565–571. Ieee, 2016. 5

    2016

  19. [27]

    Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields

    Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstanti- nos G Derpanis, Jonathan Kelly, Marcus A Brubaker, Igor Gilitschenski, and Alex Levinshtein. Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields. In CVPR, pages 20669–20679, 2023. 2

    2023

  20. [28]

    Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: rec- ommendations from the american society of echocardiogra- phy

    Carol Mitchell, Peter S Rahko, Lori A Blauwet, Barry Cana- day, Joshua A Finstuen, Michael C Foster, Kenneth Hor- ton, Kofo O Ogunyankin, Richard A Palma, and Eric J Velazquez. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: rec...

    2019

  21. [29]

    The segment anything model (sam) for remote sensing applications: From zero to one shot

    Lucas Prado Osco, Qiusheng Wu, Eduardo Lopes de Lemos, Wesley Nunes Gonc ¸alves, Ana Paula Marques Ramos, Jonathan Li, and Jos´e Marcato Junior. The segment anything model (sam) for remote sensing applications: From zero to one shot. International Journal of Applied Earth Obse...

    2023

  22. [30]

    Multi-view radar semantic segmen- tation

    Arthur Ouaknine, Alasdair Newson, Patrick P ´erez, Florence Tupin, and Julien Rebut. Multi-view radar semantic segmen- tation. In ICCV, pages 15671–15680, 2021. 2

    2021

  23. [31]

    Video-based ai for beat-to-beat assessment of cardiac func- tion

    David Ouyang, Bryan He, Amirata Ghorbani, Neal Yuan, Joseph Ebinger, Curtis P Langlotz, Paul A Heidenreich, Robert A Harrington, David H Liang, Euan A Ashley, et al. Video-based ai for beat-to-beat assessment of cardiac func- tion. Nature, 580(7802):252–256, 2020. 5

    2020

  24. [32]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, pages 234–241. Springer, 2015. 6, 7, 8

    2015

  25. [33]

    Convolu- tional neural networks in medical image understanding: a survey

    DR Sarvamangala and Raghavendra V Kulkarni. Convolu- tional neural networks in medical image understanding: a survey. Evolutionary intelligence, 15(1):1–22, 2022. 1

    2022

  26. [34]

    Autosam: Adapting sam to medical images by overloading the prompt encoder

    Tal Shaharabany, Aviad Dahan, Raja Giryes, and Lior Wolf. Autosam: Adapting sam to medical images by overloading the prompt encoder. arXiv preprint arXiv:2306.06370, 2023. 3

    2023 arXiv

  27. [35]

    Transformers in medical imaging: A survey

    Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu. Transformers in medical imaging: A survey. Medical Image Analysis, 88:102802, 2023. 1

    2023

  28. [36]

    Drcnn: Dynamic routing convolutional neural network for multi-view 3d object recognition

    Kai Sun, Jiangshe Zhang, Junmin Liu, Ruixuan Yu, and Zengjie Song. Drcnn: Dynamic routing convolutional neural network for multi-view 3d object recognition. IEEE Trans- actions on Image Processing, 30:868–877, 2020. 1

    2020

  29. [37]

    Vrp-sam: Sam with visual reference prompt

    Yanpeng Sun, Jiahui Chen, Shan Zhang, Xinyu Zhang, Qiang Chen, Gang Zhang, Errui Ding, Jingdong Wang, and Zechao Li. Vrp-sam: Sam with visual reference prompt. In CVPR, pages 23565–23574, 2024. 2

    2024

  30. [38]

    Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model

    Di Wang, Jing Zhang, Bo Du, Minqiang Xu, Lin Liu, Dacheng Tao, and Liangpei Zhang. Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model. Advances in Neural Information Processing Systems, 36, 2024. 2

    2024

  31. [39]

    Medical sam adapter: Adapting seg- ment anything model for medical image segmentation.arXiv preprint arXiv:2304.12620, 2023

    Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, and Yueming Jin. Medical sam adapter: Adapting seg- ment anything model for medical image segmentation.arXiv preprint arXiv:2304.12620, 2023. 3, 6, 7, 8

    2023 arXiv

  32. [40]

    Pa-sam: Prompt adapter sam for high-quality image segmentation

    Zhaozhi Xie, Bochen Guan, Weihao Jiang, Muyang Yi, Yue Ding, Hongtao Lu, and Lei Zhang. Pa-sam: Prompt adapter sam for high-quality image segmentation. arXiv preprint arXiv:2401.13051, 2024. 3

    2024 arXiv

  33. [41]

    Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively

    Haobo Yuan, Xiangtai Li, Chong Zhou, Yining Li, Kai Chen, and Chen Change Loy. Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively. In ECCV, pages 419–437. Springer, 2025. 2

    2025

  34. [42]

    Surgicalsam: Efficient class promptable sur- gical instrument segmentation

    Wenxi Yue, Jing Zhang, Kun Hu, Yong Xia, Jiebo Luo, and Zhiyong Wang. Surgicalsam: Efficient class promptable sur- gical instrument segmentation. In AAAI, pages 6890–6898,

  35. [43]

    Sam3d: Zero-shot 3d object detection via segment anything model

    Dingyuan Zhang, Dingkang Liang, Hongcheng Yang, Zhikang Zou, Xiaoqing Ye, Zhe Liu, and Xiang Bai. Sam3d: Zero-shot 3d object detection via segment anything model. arXiv preprint arXiv:2306.02245, 2023. 2

    2023 arXiv

  36. [44]

    Customized segment any- thing model for medical image segmentation

    Kaidong Zhang and Dong Liu. Customized segment any- thing model for medical image segmentation. arXiv preprint arXiv:2304.13785, 2023. 3, 6, 7, 8

    2023 arXiv

  37. [45]

    Cross-view discrepancy-dependency network for volumetric medical image segmentation

    Shengzhou Zhong, Wenxu Wang, Qianjin Feng, Yu Zhang, and Zhenyuan Ning. Cross-view discrepancy-dependency network for volumetric medical image segmentation. Medi- cal Image Analysis, 99:103329, 2025. 2

    2025

Pith tools

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