DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology

Haichun Yang; Kenji Ikemura; Leiyue Zhao; Mert R. Sabuncu; Paul D. Simonson; Ruining Deng; Yanfan Zhu; Yihe Yang; Yuankai Huo; Yuechen Yang

REVIEW 3 major objections 5 minor 21 references

DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read DyMorph-B2I claims that a dynamic, morphology-guided pipeline—combining watershed, skeletonization, and morphological operations with per-class hyperparameter tuning—converts binary renal-pathology masks into instance counts that match huma

desk verdict Useful engineering integration, but the evaluation is circular: parameters are tuned to match the same human counts used for scoring, so the near-perfect numbers are by construction. read the letter →

arxiv 2508.15208 v1 pith:AKDK7MC5 submitted 2025-08-21 cs.CV

Leiyue Zhao , Yuechen Yang , Yanfan Zhu , Haichun Yang , Yuankai Huo , Paul D. Simonson , Kenji Ikemura , Mert R. Sabuncu

show 2 more authors

Yihe Yang Ruining Deng

This is my paper · ORCID

classification cs.CV

keywords binary-to-instancesegmentationrenalpathologyinstancewatershedskeletonizationmathematicalmorphologymorphometricanalysishyperparametertuning

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 tackles a practical bottleneck in computational renal pathology: semantic segmentation models produce binary masks that lump many touching structures together, so object counts and per-structure measurements are unreliable. DyMorph-B2I is a post-processing pipeline that converts those binary masks into separated instances by chaining three classical techniques—watershed, skeletonization, and morphological operations—and then refining the proposed cut lines with geometry-aware rules (convex-defect depth selection, skeleton alignment, length constraints) plus per-class hyperparameter tuning. On 300 annotated ROIs spanning six renal structure classes, the paper reports an average count error of 0.16% and correlations above 0.99 with human expert counts, far below the error of each classical method alone or the naive combination. The claim matters because it offers a transparent, retraining-free route from the binary masks most datasets already provide to the instance-level quantification that diagnosis and research actually need.

What carries the argument

The load-bearing mechanism is the geometry-guided refinement step that turns raw watershed and skeleton outputs into plausible cut lines. Convex defect depth selection identifies the two deepest concavities of a candidate region and uses them as provisional endpoints; those endpoints are then compared with the region's skeleton, an approximation of the true medial axis, and shifted to align with it, so cuts follow anatomically plausible boundaries rather than arbitrary chords. A bisection-based length constraint rejects cut lines that are too short (noise) or too long (over-segmentation), and the whole configuration is searched per class over a defined hyperparameter tuple—method, distance-t

What would settle it

Run a held-out evaluation: tune the five hyperparameters per class on half of the ROIs using only those images' human counts, freeze the parameters, and apply the pipeline to the held-out images with no human reference. If the held-out MAPE climbs from 0.0016 toward the tens of percent seen for the classical baselines, the reported superiority is a tuning artifact; if it stays near 0.0016, the transfer claim holds.

Watch

Extended reading notes

Core claim

The paper claims that binary-to-instance conversion in renal pathology is not solved by any single classical operator, but can be solved by their combination under morphology-guided, image-adaptive control. In the proposed pipeline, watershed first separates roughly circular adhesions; convexity-defect analysis then finds concave junctions that are likely true boundaries, using the two deepest defects as provisional endpoints for cut lines that are aligned to the region skeleton to approximate the medial axis; morphological operations and length constraints clean spurious cuts; and a five-parameter search (segmentation method, distance-kernel size, foreground threshold, minimum and maximum c

Load-bearing premise

The load-bearing premise is that the per-class hyperparameters tuned against human object counts (Section 2.3) will transfer to new images without access to those counts, yet the paper reports no held-out split, leaving open the possibility that the near-zero errors reflect fitting to the evaluation labels rather than genuine segmentation ability.

Editorial extensions

If this is right

Any binary semantic mask of renal structures, whatever segmentation model produced it, can be upgraded to instance-level counts and geometry by a deterministic pipeline with no training—only per-class parameter values.
The same code path adapts to the morphology of each structure class, so quasi-circular objects (capsules, tufts) and elongated or densely packed ones (tubules, arteries, peritubular capillaries) are handled by the same mechanism.
Because count errors approach zero across all six classes, downstream morphometric quantities such as size, shape, and spatial distribution become computable from existing binary-mask datasets.
The pipeline is fully transparent, combining classical operators with explicit, inspectable parameters, which makes it auditable in settings where black-box components are unacceptable.
The method can generate instance pseudo-labels for training learning-based instance segmenters, converting a slow classical post-process into cheap supervision, as the paper's future-work section anticipates.

Reading between the lines

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

A fair reading of Section 2.3 suggests the 0.0016 MAPE is an optimistic bound: the tuning reference and the evaluation target are the same human counts, so a deployment protocol with parameters frozen after tuning on a labeled subset would likely show a larger—though not necessarily unattractive—error.
The dataset spans four stains (H&E, PAS, silver, trichrome) but results are pooled; whether per-class parameters transfer across stains or must be re-derived per stain is open and directly testable from the released pipeline.
The metrics count objects only; if the pipeline's real value is morphometric, per-instance boundary fidelity (for example, Dice or boundary distance) must be measured, since counting can be correct while geometry is wrong.
Deterministic and invertible in its parameters, the pipeline could serve as a pseudo-label engine to distill morphology-guided priors into a fast network, an extension of the paper's stated direction that would also make computational cost acceptable at scale.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my read on DyMorph-B2I. The core idea is a classical image-processing pipeline—watershed, skeletonization, morphology, convex-defect-guided line refinement—with per-image hyperparameter search to separate touching instances in renal pathology binary masks. That integration is real and honestly described; the components are standard and cited. The paper also ships a public GitHub repo, which is good. So there is a legitimate engineering contribution here, modest in scale.

The problem is the evaluation. Section 2.3 states that tuning is 'guided by expert anatomical knowledge, with the object count serving as a primary reference.' Figure 3 says evaluation 'was based on the object number determined by clinical anatomy knowledge, i.e., the manual annotation shown in the figure.' Section 3.2 then computes MAPE and PE against exactly those human counts. So the grid search has direct access to the test labels. With seven tunable parameters, matching a single scalar count is trivial, and the near-zero errors in Table 1 (0.0000 for CAP, TUFT, ART, PTC) are exactly what you'd expect from fitting, not from genuine segmentation ability. The baselines don't get the same label-dependent tuning, so the comparison is unfair and the claim that DyMorph-B2I 'outperforms' them is unsupported.

There's no held-out split, no description of how the tuning would work without labels at inference time, and no comparison to any learning-based method. The paper's own limitations section acknowledges accuracy drops on hard classes, but the reported numbers don't reflect that.

I don't think this is deliberate gaming; the authors are transparent about the tuning protocol. But transparency doesn't fix circularity. What's needed is a proper split: tune parameters on a validation set, then report test MAPE, or at least describe a parameter-free protocol that doesn't look at the test counts.

The paper is coherent and addresses a real problem, and the pipeline could be useful to pathologists if the evaluation is redone properly. As it stands, the title claim 'high accuracy' is not supported. For a journal, I'd send it back for major revision with a clear request for a non-circular evaluation. For a conference, it shouldn't be accepted in this form.

Referee Report

3 major / 5 minor

Summary. The paper proposes DyMorph-B2I, a pipeline that combines watershed, skeletonization, and morphological operations with image- and class-specific hyperparameter tuning to convert binary semantic masks into instance segmentations for renal pathology structures (DT, PT, CAP, TUFT, ART, PTC). The method is evaluated on 300 ROIs from the NEPTUNE project using mean absolute percentage error (MAPE) and percentage error (PE) of instance counts against human annotations. The authors report average MAPE of 0.0016 and Spearman/Pearson correlations above 0.99, and conclude that DyMorph-B2I outperforms individual classical methods and a naive combination.

Significance. If the reported evaluation were valid, DyMorph-B2I would be a useful, transparent post-processing tool for converting available binary masks into instance counts, and the public code release is valuable. However, the central evaluation is circular: hyperparameters are selected using the human object count as the reference on the same images that are later used to compute MAPE/PE. As a result, the near-perfect error values (e.g., 0.0000 for several classes in Table 1) reflect fitting to the evaluation labels rather than independent generalization. The paper's main contribution is therefore not currently supported by the evidence presented.

major comments (3)

[§2.3, §3.2, Table 1, Fig. 3] The evaluation procedure is circular. Section 2.3 states that hyperparameter tuning was 'guided by expert anatomical knowledge, with the object count serving as a primary reference for evaluating plausibility,' and Fig. 3 confirms that evaluation is based on the object number determined by manual annotation. The same human object counts are then used in Eq. (1) and Eq. (2) of Section 3.2 to compute MAPE and PE, and in Table 1 to report near-zero errors. The grid search over at least seven tunable parameters (method, dist_kernel, fg_thresh, min_len, max_len, min_area, inter_collect) has direct access to the test labels. Matching a single scalar count per image is therefore almost guaranteed, and the reported average MAPE of 0.0016 is minimized by construction rather than being an independent measure of segmentation accuracy. This invalidates the central claim in Section 4.1 that DyMorph-B
[§3.1–§3.2] No held-out validation or data split is described. The paper reports per-class statistics over 50 ROIs per class, but it never states whether the hyperparameter selection in Section 2.3 was performed on a separate tuning set, a training fold, or the same images used for evaluation. Without a visible separation between the images used for tuning and those used for evaluation, the quantitative results in Table 1 cannot be interpreted as generalization performance. The authors should provide a clear split (e.g., per-image or per-ROI cross-validation) and report MAPE/PE on held-out images only, with the tuning procedure applied without access to the human counts for those images.
[Table 1, §2.3] The comparison against baselines is unfair because DyMorph-B2I receives image-specific, label-guided tuning while the classical methods (FindContour, Watershed, Skeleton, Morphology, and the naive combination) are evaluated with fixed, presumably untuned parameters. Even if the tuning were done on a separate set, the comparison should be controlled: either tune all methods under the same protocol, or evaluate all methods with parameters selected only from a training subset. As written, the 'win' over baselines may be due entirely to access to the evaluation labels, not to the proposed integration and refinement steps.

minor comments (5)

[Fig. 5 caption] The caption states 'Positive values indicate under-segmentation by the method, while negative values indicate over-segmentation.' This appears reversed: with PE = (m_i - h_i)/h_i, a positive value means the method produced more instances than the human reference, i.e., over-segmentation, and a negative value means under-segmentation. Please correct.
[Throughout] There are several typos and formatting inconsistencies, e.g., 'W/O Tunning' in Fig. 4, 'naïve' vs. 'naive', and inconsistent use of 'Error' in Table 1 without stating that it is MAPE.
[References] Reference [17] appears to be a duplicate of reference [11]. Also, reference [22] has a typo in the author name ('be, K.' should probably be 'Abe, K.').
[§2.3] The description of 'mix params' lists 'five essential elements' and then enumerates five items (method, dist kernel, fg thresh, min len, max len), which is consistent, but the naming 'mix params' is confusing and should be defined more precisely.
[§4.1] Fig. 5 shows boxplots of PE but no statistical tests, confidence intervals, or per-image variability are provided. Since the central comparison is affected by the circularity issue, this is only a presentation concern once the evaluation protocol is fixed.

Circularity Check

1 steps flagged · score 9.0 of 10

Evaluation is circular: the per-image hyperparameter search uses the human object count as its reference, and the reported MAPE/PE and correlations are computed against exactly the same human object counts, so DyMorph-B2I's near-perfect accuracy is a fitted quantity, not a prediction.

fitted input called prediction [Section 2.3 (Dynamic and Customizable Hyperparameter Tuning), Section 3.2 (Evaluation Metrics), Table 1, Fig. 3 caption]
"Hyperparameter tuning was guided by expert anatomical knowledge, with the object count serving as a primary reference for evaluating plausibility. ... This approach enables precise and efficient instance separation ... by systematically exploring key algorithmic parameters ... via grid search or comparative testing. ... Evaluation was based on the object number determined by clinical anatomy knowledge, i.e., the manual annotation shown in the figure. ... MAPEc = 1/|Sc| sum_i |m_i - h_i| / h_i, where m_i denotes the measurement produced by the segmentation method for sample i, h_i represents th"

The grid search selects per-image hyperparameters (method, dist_kernel, fg_thresh, min_len, max_len, min_area, inter_collect) using the human object count h_i as the reference for plausibility. The performance metrics in Section 3.2 — MAPE, PE, and the correlations in Table 1 — are then evaluated against the same h_i on the same 300 images. With seven tunable parameters matched to a single scalar count, the search can almost always find a configuration whose predicted count equals h_i, so the reported average MAPE of 0.0016 is minimized by construction. The classical baselines are not given this label-dependent tuning, so the comparison in Table 1 does not demonstrate that DyMorph-B2I generalizes better; it demonstrates that the proposed method's parameters were fit to the evaluation label

full rationale

The paper's central quantitative claim — that DyMorph-B2I outperforms classical methods (Table 1, Section 4.1) — rests on an evaluation protocol in which the human object count is used both as the reference for per-image hyperparameter selection (Section 2.3) and as the ground-truth value in the MAPE/PE equations (Section 3.2, Eqs. 1-2) and in the Fig. 3 caption. This is the classic fitted-input-called-prediction pattern: the same target variable that defines success is available to the optimizer during grid search. The reported MAPE of 0.0016 and near-perfect Spearman/Pearson correlations are therefore by-construction artifacts of the tuning procedure rather than independent measurements of instance-segmentation accuracy. The paper provides no train/validation/test separation, no ablation with fixed parameters, and no external benchmark; the qualitative figures show improvements but do not rescue the quantitative evaluation from circularity. Classical methods and the naive combination are evaluated without analogous label-guided tuning, making the superiority claim unfair. Apart from this central evaluation circularity, I find no load-bearing self-citation or definitional identity in the method itself: the integration of watershed, skeletonization, and morphology is a real algorithmic contribution. The circularity score is high because the headline quantitative result reduces to a fit to the evaluation labels.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The central claim depends on a large set of hyperparameters (at least seven) that are tuned per class/image using the same object-count ground truth used for evaluation. There are no invented physical entities, but the tuning protocol and the evaluation metric are confounded, creating a high circularity burden. The axioms are domain assumptions about mask accuracy, annotation quality, and the sufficiency of object counts as the evaluation target.

free parameters (7)

dist kernel size = not reported
Controls distance-transform kernel; tuned per class/image using the human object count as reference (Section 2.3).
fg_thresh = not reported
Foreground-threshold ratio; tuned to influence object separation, with the same reference.
min_len = not reported
Lower bound on segmentation-line length; tuned to remove micro-cuts (Section 2.3).
max_len = not reported
Upper bound on segmentation-line length; tuned to avoid splitting whole regions (Section 2.3).
min_area = not reported
Minimum contour area; tuned to filter spurious objects (Section 2.3).
inter_collect = not reported
Boolean flag for extracting internal nested contours; tuned per class/image (Section 2.3).
method choice = not reported
Categorical selection among classical and hybrid techniques; part of the mix params grid search (Section 2.3).

assumptions (4)

domain assumption Input binary semantic masks are accurate; only instance separation is needed.
The entire pipeline operates on existing semantic masks and assumes their pixel-wise accuracy; invoked throughout Methods.
domain assumption The number of instances per image, as counted by expert annotators, is the correct ground truth for evaluation.
Section 3.2 defines MAPE and PE on object counts; no pixel-level ground truth is used.
domain assumption Classical algorithm implementations (watershed, skeletonization, morphology) are correct and their default parameters are the right baselines.
Baselines in Table 1 are used without optimization; no implementation details or parameter choices are given.
domain assumption The NEPTUNE dataset annotations are reliable.
Data section states expert annotators manually delineated ROIs; no inter-annotator agreement is reported.

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Cite this review

Pith. "Pith review of DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology." pith.science (2026). https://pith.science/paper/AKDK7MC5

@misc{pith2026250815208,
  author       = {Pith},
  title        = {Pith review of: DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AKDK7MC5}},
  note         = {Machine review of arXiv:2508.15208}
}

read the original abstract

Accurate morphological quantification of renal pathology functional units relies on instance-level segmentation, yet most existing datasets and automated methods provide only binary (semantic) masks, limiting the precision of downstream analyses. Although classical post-processing techniques such as watershed, morphological operations, and skeletonization, are often used to separate semantic masks into instances, their individual effectiveness is constrained by the diverse morphologies and complex connectivity found in renal tissue. In this study, we present DyMorph-B2I, a dynamic, morphology-guided binary-to-instance segmentation pipeline tailored for renal pathology. Our approach integrates watershed, skeletonization, and morphological operations within a unified framework, complemented by adaptive geometric refinement and customizable hyperparameter tuning for each class of functional unit. Through systematic parameter optimization, DyMorph-B2I robustly separates adherent and heterogeneous structures present in binary masks. Experimental results demonstrate that our method outperforms individual classical approaches and na\"ive combinations, enabling superior instance separation and facilitating more accurate morphometric analysis in renal pathology workflows. The pipeline is publicly available at: https://github.com/ddrrnn123/DyMorph-B2I.

Figures

Figures reproduced from arXiv: 2508.15208 by the authors.

**Figure 2.** The workflow of the DyMorph-B2I method. The intermediate visualizations illustrate the function and effect [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Comparison of segmentation performance across six classes using classical methods, the combination approach, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Comparative analysis of segmentation results with and without specific hyperparameter tuning. The figure reveal [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Boxplots of PE for six segmentation algorithms.Panels show three scenarios: (a) Easy Class, in which all methods [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages

[1]

Fully Convolutional Instance-Aware Semantic Segmentation ,

Li, Y ., Qi, H., Dai, J., Ji, X., and Wei, Y ., “ Fully Convolutional Instance-Aware Semantic Segmentation ,” in [2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)], 4438–4446, IEEE Computer Society, Los Alamitos, CA, USA (July 2017)

work page 2017
[2]

Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,

Zhu, J., Yin, M., Deng, R., Long, Y ., Wang, Y ., Wang, Y ., Zhao, S., Yang, H., and Huo, Y ., “Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,” in [Medical Imaging 2025: Digital and Computational Pathology],13413, 49–56, SPIE (2025)

work page 2025
[3]

Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,

Ke, J., Zhu, J., Yang, X., Zhang, H., Sun, Y ., Wang, J., Lu, Y ., Shen, Y ., Liu, S., Jiang, F., et al., “Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,”Journal of Shanghai Jiaotong University (Science)29(6), 945–957 (2024)

work page 2024
[4]

Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,

Deng, R., Liu, Q., Cui, C., Yao, T., Long, J., Asad, Z., Womick, R. M., Zhu, Z., Fogo, A. B., Zhao, S., et al., “Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,”IEEE Transactions on Biomedical Engineering70(9), 2636–2644 (2023)

work page 2023
[5]

Prpseg: Universal proposition learning for panoramic renal pathology segmentation,

Deng, R., Liu, Q., Cui, C., Yao, T., Yue, J., Xiong, J., Yu, L., Wu, Y ., Yin, M., Wang, Y ., et al., “Prpseg: Universal proposition learning for panoramic renal pathology segmentation,” in [Proceedings of the IEEE/CVF conference on computer vision and pattern recognition], 11736–11746 (2024)

work page 2024
[6]

Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,

Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L. W., Bao, S., Landman, B. A., Wheless, L. E., Coburn, L. A., Wilson, K. T., et al., “Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,” in [IS&T International Symposium on Electronic Imaging],37, COIMG–132 (2025)

work page 2025
[7]

Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,

Deng, R., Yang, Y ., Pisapia, D. J., Liechty, B. L., Zhu, J., Xiong, J., Guo, J., Lu, Z., Wang, J., Yao, X., et al., “Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,” in [Medical Imaging with Deep Learning], (2025)

work page 2025
[8]

Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,

Deng, R., Li, Y ., Li, P., Wang, J., Remedios, L. W., Agzamkhodjaev, S., Asad, Z., Liu, Q., Cui, C., Wang, Y ., et al., “Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,” in [International Conference on Medical Image Computing and Computer-Assisted Intervention], 497–507, Springer (2023)

work page 2023

Show all 21 references

[9]

Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,

Jayapandian, C. P., Chen, Y ., Janowczyk, A. R., Palmer, M. B., Cassol, C. A., Sekulic, M., Hodgin, J. B., Zee, J., Hewitt, S. M., O’Toole, J., et al., “Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple h...

2021
[10]

Deep learning–based segmentation and quantification in experimental kidney histopathology,

Bouteldja, N., Klinkhammer, B. M., B ¨ulow, R. D., Droste, P., Otten, S. W., V on Stillfried, S. F., Moellmann, J., Shee- han, S. M., Korstanje, R., Menzel, S., et al., “Deep learning–based segmentation and quantification in experimental kidney histopathology,”Journal of the A...

2021
[12]

Pyspatial: A high-speed whole slide image pathomics toolkit,

Yang, Y ., Wang, Y ., Yao, T., Deng, R., Yin, M., Zhao, S., Yang, H., and Huo, Y ., “Pyspatial: A high-speed whole slide image pathomics toolkit,”arXiv preprint arXiv:2501.06151(2025)

2025 arXiv
[13]

Spatial pathomics toolkit for quantitative analysis of podocyte nuclei with histology and spatial transcriptomics data in renal pathology,

Chen, J., Wang, Y ., Deng, R., Liu, Q., Cui, C., Yao, T., Liu, Y ., Zhong, J., Fogo, A. B., Yang, H., et al., “Spatial pathomics toolkit for quantitative analysis of podocyte nuclei with histology and spatial transcriptomics data in renal pathology,” in [Proceedings of SPIE–th...

2024
[14]

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling,

Carmichael, I., Song, A. H., Chen, R. J., Williamson, D. F. K., Chen, T. Y ., and Mahmood, F., “Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling,” (2022)

2022
[15]

Automatic detection of rare pathologies in fundus photographs using few-shot learning,

Quellec, G., Lamard, M., Conze, P.-H., Massin, P., and Cochener, B., “Automatic detection of rare pathologies in fundus photographs using few-shot learning,”Medical Image Analysis61, 101660 (Apr. 2020)

2020
[16]

Glo-in-one- v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology,

Yu, L., Yin, M., Deng, R., Liu, Q., Yao, T., Cui, C., Guo, J., Wang, Y ., Wang, Y ., Zhao, S., et al., “Glo-in-one- v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology,”Journal of Medical Imaging12(6), 061406–061406 (2025)

2025
[17]

A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images,

Jiang, L., Chen, W., Dong, B., Mei, K., Zhu, C., Liu, J., Cai, M., Yan, Y ., Wang, G., Zuo, L., and Shi, H., “A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images,”The American Journal of Pathology191(8), 1431–1441 (2021)

2021
[18]

An efficient immersion-based watershed transform method and its prototype ar- chitecture,

Rambabu, C. and Chakrabarti, I., “An efficient immersion-based watershed transform method and its prototype ar- chitecture,”Journal of Systems Architecture53(4), 210–226 (2007)

2007
[19]

Image analysis using mathematical morphology,

Haralick, R. M., Sternberg, S. R., and Zhuang, X., “Image analysis using mathematical morphology,”IEEE Transac- tions on Pattern Analysis and Machine IntelligencePAMI-9(4), 532–550 (1987)

1987
[20]

A fast parallel algorithm for thinning digital patterns,

Zhang, T. Y . and Suen, C. Y ., “A fast parallel algorithm for thinning digital patterns,”Commun. ACM27, 236–239 (Mar. 1984)

1984
[21]

Digital pathology evaluation in the multicenter nephrotic syndrome study network (neptune),

Barisoni, L., Nast, C. C., Jennette, J. C., Hodgin, J. B., Herzenberg, A. M., Lemley, K. V ., Conway, C. M., Kopp, J. B., Kretzler, M., Lienczewski, C., et al., “Digital pathology evaluation in the multicenter nephrotic syndrome study network (neptune),”Clinical Journal of the...

2013
[22]

Topological structural analysis of digitized binary images by border following,

Suzuki, S. and be, K., “Topological structural analysis of digitized binary images by border following,”Computer Vision, Graphics, and Image Processing30(1), 32–46 (1985)

1985

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Fully Convolutional Instance-Aware Semantic Segmentation ,

Li, Y ., Qi, H., Dai, J., Ji, X., and Wei, Y ., “ Fully Convolutional Instance-Aware Semantic Segmentation ,” in [2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)], 4438–4446, IEEE Computer Society, Los Alamitos, CA, USA (July 2017)

work page 2017

[2] [2]

Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,

Zhu, J., Yin, M., Deng, R., Long, Y ., Wang, Y ., Wang, Y ., Zhao, S., Yang, H., and Huo, Y ., “Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,” in [Medical Imaging 2025: Digital and Computational Pathology],13413, 49–56, SPIE (2025)

work page 2025

[3] [3]

Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,

Ke, J., Zhu, J., Yang, X., Zhang, H., Sun, Y ., Wang, J., Lu, Y ., Shen, Y ., Liu, S., Jiang, F., et al., “Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,”Journal of Shanghai Jiaotong University (Science)29(6), 945–957 (2024)

work page 2024

[4] [4]

Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,

Deng, R., Liu, Q., Cui, C., Yao, T., Long, J., Asad, Z., Womick, R. M., Zhu, Z., Fogo, A. B., Zhao, S., et al., “Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,”IEEE Transactions on Biomedical Engineering70(9), 2636–2644 (2023)

work page 2023

[5] [5]

Prpseg: Universal proposition learning for panoramic renal pathology segmentation,

Deng, R., Liu, Q., Cui, C., Yao, T., Yue, J., Xiong, J., Yu, L., Wu, Y ., Yin, M., Wang, Y ., et al., “Prpseg: Universal proposition learning for panoramic renal pathology segmentation,” in [Proceedings of the IEEE/CVF conference on computer vision and pattern recognition], 11736–11746 (2024)

work page 2024

[6] [6]

Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,

Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L. W., Bao, S., Landman, B. A., Wheless, L. E., Coburn, L. A., Wilson, K. T., et al., “Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,” in [IS&T International Symposium on Electronic Imaging],37, COIMG–132 (2025)

work page 2025

[7] [7]

Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,

Deng, R., Yang, Y ., Pisapia, D. J., Liechty, B. L., Zhu, J., Xiong, J., Guo, J., Lu, Z., Wang, J., Yao, X., et al., “Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,” in [Medical Imaging with Deep Learning], (2025)

work page 2025

[8] [8]

Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,

Deng, R., Li, Y ., Li, P., Wang, J., Remedios, L. W., Agzamkhodjaev, S., Asad, Z., Liu, Q., Cui, C., Wang, Y ., et al., “Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,” in [International Conference on Medical Image Computing and Computer-Assisted Intervention], 497–507, Springer (2023)

work page 2023