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REVIEW 4 major objections 4 minor 52 references

SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that self-supervised pretraining on large unlabeled light-sheet microscopy datasets improves segmentation accuracy and generalization to unseen structures.

desk verdict A genuinely useful benchmark resource whose abstract and conclusion overstate what the single mixed ablation supports. read the letter →

arxiv 2501.03880 v2 pith:5WXMR5SY submitted 2025-01-07 eess.IV cs.CVcs.LG

Ying Chen , Rami Al-Maskari , Izabela Horvath , Mayar Ali , Luciano Hoher , Kaiyuan Yang , Zengming Lin , Zhiwei Zhai
show 9 more authors
Mengzhe Shen Dejin Xun Yi Wang Tony Xu Maged Goubran Yunheng Wu Kensaku Mori Johannes C. Paetzold Ali Erturk
This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords light-sheetmicroscopyself-supervisedlearning3DimagesegmentationMICCAIchallengemicrogliadomaingeneralizationcontrastivemaskedmodeling
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 presents the SELMA3D challenge, held at MICCAI 2024, as the first community-scale test of self-supervised learning for 3D light-sheet microscopy segmentation. Its claim is that pretraining on large unlabeled images of cleared mouse and human brains produces segmentation models that perform as well as fully supervised baselines and, crucially, generalize to biological structures absent from the labeled training data, such as microglia. The dataset consists of 35 large 3D volumes and 315 annotated patches, and the five submitted methods use contrastive learning, masked volume inpainting, and 3D-adapted DINOv2/iBOT. The paper reads the results as showing that self-supervised learning improves generalization for tree-like structures, while its effect on spot-like structures is mixed, and it concludes that SSL strategies should be tailored to structure type. If the claim holds, researchers with large archives of unlabeled light-sheet data could reduce annotation costs and build models that transfer across distinct biological structures.

What carries the argument

The carrying mechanism is the challenge's data architecture: a large unlabeled pretraining corpus of 35 whole-brain light-sheet volumes, a small supervised fine-tuning set, and held-out test patches that include microglia, a structure absent from the annotated training data. Evaluation uses volumetric Dice, Betti-number errors in dimensions 0 and 1, and centerline Dice, forcing a method to preserve both voxel overlap and topology. The teams' self-supervised methods—BYOL, SimCLR, masked volume inpainting, and 3D DINOv2/iBOT—are the independent variable, and the Zoomlin team's frozen-encoder 3D U-Net tested with and without SSL is the only controlled comparison that isolates the pretraining effect.

What would settle it

A controlled rerun of each challenge pipeline on the final test set, with identical architecture and matched training budget and with the only difference being whether SSL pretraining was applied, would settle the claim; if no-SSL versions match or beat SSL versions on unseen structures, the central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that self-supervised learning on large, unannotated 3D light-sheet microscopy volumes improves segmentation performance and generalization compared with fully supervised training. The SELMA3D challenge is the vehicle for this claim: the organizers release 35 large cleared-brain images (blood vessels, c-Fos+ cells, cell nuclei, and amyloid-beta plaques) without labels for pretraining, plus 315 annotated patches for fine-tuning and evaluation. Five teams submitted full pipelines based on BYOL, SimCLR, masked volume inpainting, and a 3D adaptation of DINOv2/iBOT. The winning team reached 75.00% Dice on c-Fos+ cells and 71.50% Dice on microglia in the preliminary test phase, and 65.37% and 65.17% respectively in the final test phase, where microglia are an unseen structure. The only direct with/without SSL comparison, from the Zoomlin team on the preliminary test set, shows SSL raising microglia Dice from 69.87% to 71.50% and reducing Betti-number errors, while c-Fos+ Dice falls slightly from 76.43% to 75.00%; the paper concludes from this that SSL improves robustness and generalization but is structure-dependent, motivating structure-specific pretext tasks.

Load-bearing premise

The load-bearing premise is that the Zoomlin team's with/without SSL comparison isolates the effect of self-supervised learning; the paper does not document the no-SSL model's training budget, hyperparameters, or early stopping, and the final test phase contains only eight patches per class.

Editorial extensions

If this is right

  • Self-supervised pretraining should become a default first stage for light-sheet microscopy segmentation pipelines, especially when target structures are tree-like.
  • Pretrained models can segment structures that never appear in the labeled training set, which is precisely the domain-shift case where models trained from scratch lose accuracy.
  • The benefit is not uniform: the same SSL strategy helped microglia segmentation but slightly hurt c-Fos+ cell segmentation, so pretraining choices should be matched to structure morphology.
  • Large unannotated light-sheet archives, like the 35 cleared-brain volumes released here, can serve as a pretraining resource without additional annotation cost.
  • Evaluation of such models should report overlap, topological, and centerline metrics separately, because a method can improve Dice while worsening Betti-number errors, as the preliminary results show.

Reading between the lines

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

  • A natural next experiment is shape-aware pretext tasks that reconstruct vessel bifurcations or count spot-like objects, since the paper's structure-dependent results suggest no single SSL strategy will dominate both morphology types.
  • Because only one team supplied a no-SSL baseline, a stronger benchmark design would require every participant to submit a matched no-SSL run with the same training budget and early stopping; the released infrastructure could support that as a standard protocol.
  • Beyond segmentation, the same unlabeled cleared-tissue volumes could support SSL pretraining for detection, registration, or denoising tasks in microscopy, extending the resource's value.
  • If the effect holds at larger test-set sizes, labs already collecting cleared-tissue images could amortize annotation effort by pretraining once on their full archives and fine-tuning per project.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper reports on the SELMA3D challenge held at MICCAI 2024, whose goal is to evaluate self-supervised learning (SSL) for 3D light-sheet microscopy (LSM) image segmentation. The organizers provide 35 large unlabeled 3D LSM images from cleared mouse and human brains, together with 315 (claimed) annotated patches covering vessel-like and spot-like structures. Five teams completed both the preliminary and final test phases, and the paper summarizes their SSL strategies, fine-tuning approaches, and quantitative results on c-Fos+ cell and microglia segmentation. The central claim is that SSL on large datasets improves segmentation performance and generalization, supported mainly by a with/without SSL comparison provided by the first-ranked Zoomlin team plus the overall ranking of teams.

Significance. The challenge dataset and benchmark are potentially valuable community resources: they are among the first to target SSL for 3D LSM data, include a large unlabeled corpus across four biological structures, and introduce an unseen microglia structure in the test phase to probe generalization. The paper also documents five practical SSL pipelines, which may benefit practitioners. However, the headline claim that SSL improves segmentation performance and generalization is not established by the presented evidence. The only controlled ablation is mixed, no other team ran a fully supervised baseline, and the final test set is very small with large variance. If the claim were supported, the paper would make a strong case for SSL pretraining as a default in LSM segmentation; in its current form, the evidence supports a more modest, structure-dependent conclusion.

major comments (4)
  1. [Abstract; Section 6] The abstract and conclusion state that self-supervised learning improves segmentation performance and generalization, but the only controlled evidence is the Zoomlin with/without SSL comparison in Table 6. That comparison is mixed: for c-Fos+ cells, SSL slightly decreases Dice (76.43 to 75.00) and slightly increases Betti-0 error (38.30 to 38.48), while for microglia it improves Dice (69.87 to 71.50), clDice (68.08 to 74.10), and Betti errors. No other team provided a no-SSL baseline, so cross-team rankings in Tables 4 and 5 cannot isolate SSL from architecture, preprocessing, and training-budget differences. The wording 'most participating teams demonstrate' is therefore not supported; the claims should be restricted to the Zoomlin ablation and explicitly acknowledged as mixed and non-generalizable.
  2. [Section 5.3; Table 6] The 'without SSL' comparison is not a controlled ablation as reported. The paper gives no details about the without-SSL Zoomlin model's training budget (number of iterations or epochs), optimizer, learning rate, early stopping, data augmentation, or whether the same frozen-encoder/decoder fine-tuning protocol was used. Without this information, the microglia improvements in Table 6 could arise from training-protocol differences rather than from SSL. The authors should provide full implementation details for both models or explicitly state that the comparison is not controlled and should be interpreted with caution.
  3. [Section 5.2; Table 5; Table 2] The claim of 'exceptional generalizability' for the top model on unseen microglia rests on only 8 final-test patches per class (Table 2), and the reported metrics show very large standard deviations (e.g., Zoomlin c-Fos+ Dice 65.37 ± 13.38, Betti-0 error 157.9 ± 169.9; microglia Betti-1 error 1.000 ± 0.866). With n=8 and no significance tests or confidence intervals, the ranking and the generalization conclusions are not robust. The paper should either provide statistical analysis appropriate to the sample size or substantially soften the generalization claims.
  4. [Section 5.3; Section 6] The paper is internally inconsistent about the effect of SSL. Section 5.3 concludes that SSL 'enhanced performance for tree-like structure segmentation, but led to a reduction in accuracy for spot-like structure segmentation,' and proposes structure-specific SSL strategies. Section 6, however, first states that 'the participants methods leveraging self-supervised learning achieved better performance' and then acknowledges that benefits do not always translate across structure types. These statements should be reconciled; the conclusion should follow the mixed evidence in Table 6 rather than asserting an overall benefit.
minor comments (4)
  1. [Abstract; Table 2] The abstract and text state that the dataset includes '315 annotated small patches,' but the numbers in Table 2 sum to 213 (24+19+12+34+23+85+8+8). Please verify the total or correct the table.
  2. [Throughout] There are several typographical errors that should be fixed, including 'anottations' in the Table 1 and Table 2 captions, 'prelimitary' in the Table 4 title, 'Image Smage Segmentation' in Section 2.1, 'succesful' in Section 6, 'submmited' in Section 7, and the spacing in numbers such as '34 .09' in Table 4.
  3. [Section 4.2.4] The team name in the Section 4.2.4 heading is written as 'T onyxu' with an extra space, which should be corrected to 'Tonyxu'.
  4. [Table 3] The table is dense and the fine-tuning data preprocessing column is left blank for some teams. It would be helpful to explicitly state 'none' or 'not reported' so readers know whether preprocessing was absent or simply omitted.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the paper is an empirical challenge report; the SSL claim is supported by a single mixed ablation, which is a sufficiency problem, not a circularity.

full rationale

The paper does not contain a derivation chain in which a prediction is equivalent to its inputs by construction. The central claim, that self-supervised learning improves segmentation performance and generalization, is an empirical statement evaluated on the SELMA3D challenge data. The only controlled evidence is the Zoomlin team's with/without SSL comparison in Table 6, which is mixed: SSL improves microglia Dice and beta0 error but slightly reduces c-Fos+ Dice. Because the data could have shown the opposite, the conclusion is not forced by definition, by a fitted parameter, or by a uniqueness theorem. The conclusion's generalization to 'most participating teams' and 'the participants methods' overstates the evidence, since only one team supplied a no-SSL baseline, but this is a correctness/evidence-sufficiency concern explicitly excluded from circularity scoring by the review rules. The paper cites prior works by the same authors for data acquisition and preprocessing, but those citations are background information for dataset construction, not load-bearing support for the SSL claim. No equation, self-citation chain, or ansatz is used to redefine the result as its own input. The modest score of 1 reflects the self-referential nature of an organizing group reporting results on its own challenge with a single participant-supplied ablation, not any demonstrated circular step.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new mathematical entities. It rests on domain assumptions about annotation quality, metric validity, and transfer learning. One hand-chosen preprocessing threshold appears in a participant's method.

free parameters (1)
  • Foreground intensity threshold = 500 (arbitrary units)
    Used by the Tonyxu team to filter pretraining patches by foreground content. It is a hand-chosen value, but it is not load-bearing for the paper's overall claim.
assumptions (3)
  • domain assumption Manual VR annotations reviewed by four annotators and two senior experts are reliable ground truth.
    Section 3.3 describes the annotation hierarchy but does not report inter-annotator agreement or error analysis.
  • domain assumption Dice, Betti number error, and centerline Dice capture task-relevant segmentation quality.
    Section 3.5 defines the metrics but provides no validation that they align with biological or clinical usefulness.
  • domain assumption The unlabeled large images and the annotated patches come from sufficiently related acquisition pipelines for SSL pretraining to transfer.
    The challenge design assumes improvements in representation learned on large images will transfer to small patches and to an unseen structure, microglia.

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Cite this review

Pith. "Pith review of SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation." pith.science (2026). https://pith.science/paper/5WXMR5SY

@misc{pith2026250103880,
  author       = {Pith},
  title        = {Pith review of: SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5WXMR5SY}},
  note         = {Machine review of arXiv:2501.03880}
}
read the original abstract

Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Combined with the progress in large-scale data analysis, driven by deep learning, these innovations empower researchers to rapidly investigate the morphological and functional properties of diverse biological samples. Segmentation, a crucial preliminary step in the analysis process, can be automated using domain-specific deep learning models with expert-level performance. However, these models exhibit high sensitivity to domain shifts, leading to a significant drop in accuracy when applied to data outside their training distribution. To address this limitation, and inspired by the recent success of self-supervised learning in training generalizable models, we organized the SELMA3D Challenge during the MICCAI 2024 conference. SELMA3D provides a vast collection of light-sheet images from cleared mice and human brains, comprising 35 large 3D images-each with over 1000^3 voxels-and 315 annotated small patches for finetuning, preliminary testing and final testing. The dataset encompasses diverse biological structures, including vessel-like and spot-like structures. Five teams participated in all phases of the challenge, and their proposed methods are reviewed in this paper. Quantitative and qualitative results from most participating teams demonstrate that self-supervised learning on large datasets improves segmentation model performance and generalization. We will continue to support and extend SELMA3D as an inaugural MICCAI challenge focused on self-supervised learning for 3D microscopy image segmentation.

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Works this paper leans on

52 extracted references · 26 canonical work pages

  1. [1]

    Yoshii, N

    T. Yoshii, N. Oishi, K. Ikoma, et al. , ``Brain atrophy in the visual cortex and thalamus induced by severe stress in animal model,'' Scientifc Reports 7 , 12731 (2017). [https://doi.org/10.1038/s41598-017-12917-z]

    work page doi:10.1038/s41598-017-12917-z 2017

  2. [2]

    S. Jahn, M. Plass, and F. Moinfar, ``Digital pathology: advantages, limitations and emerging perspectives,'' Journal of clinical medicine 9(11) , 3697 (2020). [https://doi.org/10.3390/jcm9113697]

    work page doi:10.3390/jcm9113697 2020

  3. [3]

    A. Maes, C. Pestiaux, A. Marino, et al. , ``Cryogenic contrast-enhanced microct enables nondestructive 3d quantitative histopathology of soft biological tissues,'' Nature Communications 13 , 6207 (2022). [https://doi.org/10.1038/s41467-022-34048-4]

    work page doi:10.1038/s41467-022-34048-4 2022

  4. [4]

    Stelzer, F

    E. Stelzer, F. Strobl, B. Chang, et al. , ``Light sheet fluorescence microscopy,'' Nature Reviews Methods Primers 1(1) , 73 (2021). [https://doi.org/10.1038/s43586-021-00069-4]

    work page doi:10.1038/s43586-021-00069-4 2021

  5. [5]

    H. Ueda, A. Ertürk, K. Chung, et al. , ``Tissue clearing and its applications in neuroscience,'' Nature Reviews Neuroscience 21(2) , 61--79 (2020). [https://doi.org/10.1038/s41583-019-0250-1]

    work page doi:10.1038/s41583-019-0250-1 2020

  6. [6]

    Poola, M

    P. Poola, M. Afzal, Y. Yoo, et al. , ``Light sheet microscopy for histopathology applications,'' Biomedical Engineering Letters 9 , 279--291 (2019). [https://doi.org/10.1007/s13534-019-00122-y]

    work page doi:10.1007/s13534-019-00122-y 2019

  7. [7]

    H. Ueda, H. Dodt, P. Osten, et al. , ``Whole-brain profiling of cells and circuits in mammals by tissue clearing and light-sheet microscopy.,'' Neuron 106(3) , 369--387 (2020). [https://doi.org/10.1016/j.neuron.2020.03.004]

    work page doi:10.1016/j.neuron.2020.03.004 2020

  8. [8]

    Zhang, A

    D. Zhang, A. Cleveland, E. Krimitza, et al. , ``Spatial analysis of tissue immunity and vascularity by light sheet fluorescence microscopy,'' Nature Protocols 19 , 1053–1082 (2024). [https://doi.org/10.1038/s41596-023-00941-5]

    work page doi:10.1038/s41596-023-00941-5 2024

Show all 52 references
  1. [9]

    Almagro, H

    J. Almagro, H. Messal, M. Thin, et al. , ``Tissue clearing to examine tumour complexity in three dimensions,'' Nature Reviews Cancer 21(11) , 718--730 (2021). [https://doi.org/10.1038/s41568-021-00382-w]

    2021 doi

  2. [10]

    P. Fei, J. Lee, R. Packard, et al. , ``Cardiac light-sheet fluorescent microscopy for multi-scale and rapid imaging of architecture and function,'' Scientific Reports 6 , 22489 (2016). [https://doi.org/10.1038/srep22489]

    2016 doi

  3. [11]

    T. Tian, Z. Yang, and X. Li, ``Tissue clearing technique: Recent progress and biomedical applications,'' Journal of anatomy 238(2) , 489--507 (2021). [https://doi.org/10.1111/joa.13309]

    2021 doi

  4. [12]

    F. Amat, B. Höckendorf, Y. Wan, et al. , ``Efficient processing and analysis of large-scale light-sheet microscopy data,'' Nature protocols 10 , 1679–1696 (2015). [https://doi.org/10.1038/nprot.2015.111]

    2015 doi

  5. [13]

    Kumar, P

    N. Kumar, P. Hrobar, M. Vagenknecht, et al. , ``A light sheet fluorescence microscopy and machine learning-based approach to investigate drug and biomarker distribution in whole organs and tumors,'' bioRxiv , 2023.09.16.558068 (2023). [https://doi.org/10.1101/2023.09.16.558068]

    2023 doi

  6. [14]

    Todorov, J

    M. Todorov, J. Paetzold, O. Schoppe, et al. , ``Machine learning analysis of whole mouse brain vasculature,'' Nature Methods 17 , 442--449 (2020). [https://doi.org/10.1038/s41592-020-0792-1]

    2020 doi

  7. [15]

    S. Shit, J. Paetzold, A. Sekuboyina, et al. , ``cldice - a novel topology-preserving loss function for tubular structure segmentation,'' IEEE/CVF Conference on Computer Vision and Pattern Recognition , 16555--16564 (2021). [https://doi.org/10.1109/CVPR46437.2021.01629]

    2021

  8. [16]

    Y. Zhou, M. Chia, S. Wagner, et al. , ``A foundation model for generalizable disease detection from retinal images,'' Nature 622 , 156--163 (2023). [https://doi.org/10.1038/s41586-023-06555-x]

    2023 doi

  9. [17]

    Touvron, L

    H. Touvron, L. Martin, K. Stone, et al. , ``Llama 2: Open foundation and fine-tuned chat models,'' arXiv preprint arXiv , 2307.09288 (2023). [https://doi.org/10.48550/arXiv.2307.09288]

    2023 doi arXiv

  10. [18]

    W. Wang, J. Dai, Z. Chen, et al. , ``Internimage: Exploring large-scale vision foundation models with deformable convolutions,'' Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 14408--14419 (2023). [https://doi.org/10.1109/CVPR52729.2023.01385]

    2023

  11. [19]

    Awais, M

    M. Awais, M. Naseer, S. Khan, et al. , ``Robust speech recognition via large-scale weak supervision,'' International conference on machine learning , 28492--28518 (2023). [https://dl.acm.org/doi/10.5555/3618408.3619590]

    2023

  12. [20]

    Krishnan, P

    R. Krishnan, P. Rajpurkar, and E. Topol, ``Self-supervised learning in medicine and healthcare,'' Nature Biomedical Engineering 6 , 1346--1352 (2022). [https://doi.org/10.1038/s41551-022-00914-1]

    2022 doi

  13. [21]

    Zhang and M

    S. Zhang and M. Dimitris, ``On the challenges and perspectives of foundation models for medical image analysis,'' Medical image analysis 91 , 102996 (2024). [https://doi.org/10.1016/j.media.2023.102996]

    2024

  14. [22]

    T. Zhao, Y. Gu, J. Yang, et al. , ``A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,'' Nature Methods (2024). [https://doi.org/10.1038/s41592-024-02499-w]

    2024 doi

  15. [23]

    T. Chen, S. Kornblith, M. Norouzi, et al. , ``A simple framework for contrastive learning of visual representations,'' International conference on machine learning , 1597--1607 (2020). [https://dl.acm.org/doi/10.5555/3524938.3525087]

    2020

  16. [25]

    Oquab, T

    M. Oquab, T. Darcet, T. Moutakanni, et al. , ``Dinov2: Learning robust visual features without supervision,'' arXiv preprint arXiv , 2304.07193 (2023). https://doi.org/10.48550/arXiv.2304.07193

    2023 doi arXiv

  17. [26]

    Blain, G

    R. Blain, G. Couly, E. Shotar, et al. , ``A tridimensional atlas of the developing human head,'' Cell 186(26) , 5910--5924 (2023). https://doi.org/10.1016/j.cell.2023.11.013

    2023 doi

  18. [27]

    Kaltenecker, R

    D. Kaltenecker, R. Al-Maskari, M. Negwer, et al. , ``Virtual reality empowered deep learning analysis of brain activity,'' Nature Methods 21 , 1306–1315 (2024). [https://doi.org/10.1038/s41592-024-02245-2]

    2024 doi

  19. [28]

    C. Pan, O. Schoppe, A. Parra-Damas, et al. , ``Deep learning reveals cancer metastasis and therapeutic antibody targeting in the entire body,'' Cell 179(7) , 1661--1676 (2019). [https://doi.org/10.1016/j.cell.2019.11.013]

    2019 doi

  20. [29]

    Y. Wang, Q. Li, L. Liu, et al. , ``Teravr empowers precise reconstruction of complete 3-d neuronal morphology in the whole brain,'' Nature Communications 10 , 3474 (2019). [https://doi.org/10.1038/s41467-019-11443-y]

    2019 doi

  21. [30]

    Packard, K

    R. Packard, K. Baek, T. Beebe, et al. , ``Automated segmentation of light-sheet fluorescent imaging to characterize experimental doxorubicin-induced cardiac injury and repair,'' Scientific Reports 7 , 8603 (2017). [https://doi.org/10.1038/s41598-017-09152-x]

    2017 doi

  22. [31]

    Spangenberg, N

    P. Spangenberg, N. Hagemann, A. Squire, et al. , ``Rapid and fully automated blood vasculature analysis in 3d light-sheet image volumes of different organs,'' Cell Reports Methods 3(3) , 100436 (2023). [https://doi.org/10.1016/j.crmeth.2023.100436]

    2023

  23. [32]

    Friedmann, A

    D. Friedmann, A. Pun, E. Adams, et al. , ``Mapping mesoscale axonal projections in the mouse brain using a 3d convolutional network,'' Proceedings of the National Academy of Sciences 117(20) , 11068--11075 (2020). [https://doi.org/10.1073/pnas.1918465117]

    2020 doi

  24. [33]

    Kirillov, E

    A. Kirillov, E. Mintun, N. Ravi, et al. , ``Segment anything,'' Proceedings of the IEEE/CVF International Conference on Computer Vision , 4015--4026 (2023). [https://doi.org/10.1109/ICCV51070.2023.00371]

    2023

  25. [34]

    J. Gui, T. Chen, J. Zhang, et al. , ``A survey on self-supervised learning: Algorithms, applications, and future trends,'' IEEE Transactions on Pattern Analysis and Machine Intelligence 46(12) , 9052--9071 (2024). [https://doi.org/10.1109/TPAMI.2024.3415112]

    2024

  26. [35]

    Taleb, W

    A. Taleb, W. Loetzsch, N. Danz, et al. , ``3d self-supervised methods for medical imaging,'' Advances in Neural Information Processing Systems , 18158--18172 (2020). [https://dl.acm.org/doi/abs/10.5555/3495724.3497248]

    2020

  27. [36]

    K. Tian, Y. Jiang, Q. Diao, et al. , ``Designing bert for convolutional networks: Sparse and hierarchical masked modeling,'' arXiv preprint arXiv , 2301.03580 (2023). https://doi.org/10.48550/arXiv.2301.03580

    2023 doi arXiv

  28. [37]

    R. Chen, T. Ding, M. Lu, et al. , ``Towards a general-purpose foundation model for computational pathology,'' Nature Medicine 30(3) , 850--862 (2024). [https://doi.org/10.1038/s41591-024-02857-3]

    2024 doi

  29. [38]

    H. Xu, N. Usuyama, J. Bagga, et al. , ``A whole-slide foundation model for digital pathology from real-world data,'' Nature 630 , 181--188 (2024). [https://doi.org/10.1038/s41586-024-07441-w]

    2024 doi

  30. [39]

    Achard, T

    C. Achard, T. Kousi, M. Frey, et al. , ``Cellseg3d: self-supervised 3d cell segmentation for microscopy,'' bioRxiv (2024). [https://doi.org/10.1101/2024.05.17.594691]

    2024 doi

  31. [40]

    Zheng, X

    Z. Zheng, X. Yue, J. Wang, et al. , ``A framework for general-purpose microscopic image analysis via self-supervised learning,'' Materials Characterization 213 , 114003 (2024). [https://doi.org/10.1016/j.matchar.2024.114003]

    2024

  32. [41]

    S. Zhao, M. Todorov, R. Cai, et al. , ``Cellular and molecular probing of intact human organs,'' Cell 180(4) , 796--812 (2020). [https://doi.org/10.1016/j.cell.2020.01.030]

    2020 doi

  33. [42]

    Bhatia, A

    H. Bhatia, A. Brunner, F. Öztürk, et al. , ``Spatial proteomics in three-dimensional intact specimens,'' Cell 185(26) , 5040--5058 (2022). [https://doi.org/10.1016/j.cell.2022.11.021]

    2022 doi

  34. [43]

    Ertürk, K

    A. Ertürk, K. Becker, N. Jährling, et al. , ``Three-dimensional imaging of solvent-cleared organs using 3disco,'' Nature Protocols 7 , 1983--1995 (2012). [https://doi.org/10.1038/nprot.2012.119]

    2012 doi

  35. [44]

    Stucki, J

    N. Stucki, J. Paetzold, S. Shit, et al. , ``Topologically faithful image segmentation via induced matching of persistence barcodes,'' International Conference on Machine Learning , 32698--32727 (2023). [https://dl.acm.org/doi/abs/10.5555/3618408.3619764]

    2023

  36. [45]

    Grill, F

    J. Grill, F. Strub, F. Altché, et al. , ``Bootstrap your own latent a new approach to self-supervised learning,'' Advances in neural information processing systems , 21271--21284 (2020). [https://dl.acm.org/doi/abs/10.5555/3495724.3497510]

    2020

  37. [46]

    Isensee, P

    F. Isensee, P. Jaeger, S. Kohl, et al. , ``nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,'' Nature Methods 18 , 203–211 (2021). [https://doi.org/10.1038/s41592-020-01008-z]

    2021 doi

  38. [47]

    Y. Tang, D. Yang, W. Li, et al. , ``Self-supervised pre-training of swin transformers for 3d medical image analysis,'' Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 20730–20740 (2022). [https://doi.org/10.1109/CVPR52688.2022.02007]

    2022

  39. [48]

    T. Lin, P. Goyal, R. Girshick, et al. , ``Focal loss for dense object detection,'' arXiv preprint arXiv , 1708.02002 (2017). [https://doi.org/10.48550/arXiv.1708.02002]

    2017 doi arXiv

  40. [49]

    Kervadec, J

    H. Kervadec, J. Bouchtiba, C. Desrosiers, et al. , ``Boundary loss for highly unbalanced segmentation,'' Medical Image Analysis 67 , 101851 (2021). [https://doi.org/10.1016/j.media.2020.101851]

    2021

  41. [50]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al. , ``An image is worth 16x16 words: Transformers for image recognition at scale,'' arXiv preprint arXiv , 2010.11929 (2020). [https://doi.org/10.48550/arXiv.2010.11929]

    2020 doi arXiv

  42. [51]

    J. Zhou, C. Wei, H. Wang, et al. , ``ibot: Image bert pre-training with online tokenizer,'' arXiv preprint arXiv , 2111.07832 (2021). [https://doi.org/10.48550/arXiv.2111.07832]

    2021 doi arXiv

  43. [52]

    Z. Chen, Y. Duan, W. Wang, et al. , ``Vision transformer adapter for dense predictions,'' arXiv preprint arXiv , 2205.08534 (2022). [https://doi.org/10.48550/arXiv.2205.08534]

    2022 doi arXiv

  44. [53]

    write newline

    " write newline "" before.all 'output.state := FUNCTION blank.sep after.quote 'output.state := FUNCTION fin.entry output.state after.quoted.block = 'skip 'add.period if write newline FUNCTION new.block output.state before.all = 'skip output.state after.quote = after.quoted.blo...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.