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REVIEW 4 major objections 5 minor 69 references

Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Adding a deep-learning z-disc classifier and ensemble graph scoring to SarcGraph makes sarcomere detection reliable in immature hiPSC-CMs, recovers longer myofibrils, and yields features that predict expert organization scores and support…

desk verdict A real engineering improvement to SarcGraph with credible held-out score predictions, but the headline claim of fewer false positives is never directly measured. read the letter →

arxiv 2501.18714 v1 pith:QD6NCRCH submitted 2025-01-30 q-bio.QM

Saeed Mohammadzadeh , Emma Lejeune This is my paper · ORCID
classification q-bio.QM
keywords hiPSC-CMSarcGraphsarcomeredetectionz-discclassificationdeeplearningstructuralorganizationexplainableclusteringexpertscoringbias
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 authors set out to make SarcGraph, a graph-based tool that detects individual sarcomeres in heart-cell images, work on immature and mature hiPSC-CMs alike. They find that the original pipeline over-reports z-discs in immature cells, where puncta and z-bodies are common, and under-detects long myofibril chains because of its local edge-scoring rule. Their fix is a deep-learning z-disc classifier that filters contour candidates plus a graph-scoring ensemble and a myofibril-extension step. On roughly 7,000 expert-scored cells from a public hiPSC-CM dataset, the modified pipeline cuts mean sarcomere counts in low-score cells from about 192 to 15, raises mean myofibril length in every score group, and produces per-sarcomere features that predict expert organization scores with Pearson correlations of 0.77 on held-out FISH images and 0.79 on held-out live images. These results give cardiac researchers a scalable, object-level measure of structural organization that can also flag bias in manual scoring.

What carries the argument

The load-bearing object is the modified SarcGraph pipeline itself: a two-phase graph-based detector in which detected z-discs are nodes and potential sarcomeres are edges. The new machinery has two parts. A deep-learning z-disc classifier, an ensemble of a contrastively pretrained convolutional model and a vision-transformer feature extractor with a small classifier head, assigns each contour a probability of being a true z-disc; the 0.3 threshold replaces the original length-only filter. An ensemble graph-scoring scheme, combining the original edge score, pruning validity, average node z-disc probability, and global myofibril alignment through probabilistic ensemble averaging, plus a rule-based myofibril-extension post-process, replaces the single local edge score. The classifier supplies the probability inputs used both for filtering and for edge scoring, so the deep-learning step is the hinge of the whole pipeline.

What would settle it

Apply the modified pipeline to a held-out set of immature hiPSC-CM images with expert-visible z-discs, and compare its z-disc and myofibril output against a version in which a human or a learned segmenter guarantees every expert-visible z-disc becomes a contour. If the guaranteed-contour version finds many more true myofibrils or much higher feature correlations with expert scores, then the claim that the deep-learning classifier fixes detection on top of Otsu is the part that fails.

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Extended reading notes

Core claim

The central claim is that two targeted changes make SarcGraph reliable on immature as well as mature cells. First, every contour produced by Laplacian-of-Gaussian filtering and Otsu thresholding is scored by an ensemble of two deep-learning classifiers, and contours with predicted z-disc probability below 0.3 are discarded; the surviving contours are also re-centered at local intensity peaks. Second, the sarcomere graph is scored by four complementary methods, the original local edge score, pruning validity, node z-disc probabilities, and a global myofibril-alignment score, fused by probabilistic ensemble averaging, then post-processed by a myofibril-extension step that bridges breaks at crossings or missed z-discs. The authors report that in low-organization cells mean detected sarcomeres drop from 192 to 15 per cell, while mean myofibril length rises in every expert-score group, for example from 1.95 to 4.75 sarcomeres in low-score cells. The extracted per-sarcomere features predict expert organization scores with Pearson correlations of 0.85 on the training set, 0.77 on held-out FISH cells, and 0.79 on held-out live cells.

Load-bearing premise

Everything downstream depends on Otsu thresholding and contour detection turning a real z-disc into a contour in the first place; the deep-learning classifier can only filter or re-center contours that already exist, and the paper notes that Otsu occasionally misses z-discs visible to expert observers because of image-brightness variation.

Editorial extensions

If this is right

  • Sarcomere count becomes a usable organization feature: low-score cells now yield near-zero detections instead of hundreds of false positives, so the feature separates low from high organization without expert input.
  • Myofibril length becomes a usable maturity readout: mean detected myofibril length rises in every score group, reaching maxima over 12 to 17 sarcomeres, so short-chain fragmentation is no longer conflated with immaturity.
  • Per-cell feature vectors from individual sarcomeres predict expert organization scores on unseen data, with Pearson r of 0.77 on test FISH and 0.79 on test live, making image-level scoring replaceable by an automated pipeline.
  • A two-feature decision tree using z-disc classification ratio and sarcomere density reproduces expert-based low, medium, and high categories with correlations comparable to or better than the supervised SVR on test data, giving a label-free, explainable alternative.
  • Feature-space analysis exposes systematic expert bias: medium- and high-score cells shift between training and test sets, and the model under-predicts test scores, so the pipeline can be used to audit manual scoring.

Reading between the lines

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

  • The same Otsu-first architecture means the next bottleneck is contour recall; replacing Otsu with a learned segmentation, such as the kind of U-Net used by prior tools, could let the classifier recover dim z-discs that are currently missed.
  • Because the two-feature decision tree labels cells without expert scores, it could serve as a weak label for training future models on the full roughly 31,000-cell dataset, not just the expert-scored subset.
  • The roughly 10 minutes per cell image makes full video-scale contraction analysis impractical; the ensemble scoring could be distilled into a single lightweight score or run on downsampled graphs to regain the speed of the original SarcGraph.
  • The same per-sarcomere features could be aligned with the transcript measurements in the public dataset to ask which genes correlate with the structural labels, something the paper's focus on image-derived scores leaves open.
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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 / 5 minor

Summary. The manuscript extends the SarcGraph framework for quantifying sarcomere organization in hiPSC-CM images by adding a deep learning-based z-disc classifier, a z-disc location correction step, an ensemble graph scoring method with probabilistic averaging, and a myofibril extension post-processing step. The modified pipeline is applied to roughly 7,000 expert-scored cells from the Allen Institute dataset, from which nine structural features per cell are extracted and used to train an SVR model that predicts organization scores with Pearson correlations of 0.85 on Train, 0.77 on Test FISH, and 0.79 on Test Live. The authors additionally apply ExKMC explainable clustering to assign low/medium/high organization labels. The central claim is that the modified pipeline significantly reduces false positive sarcomere detections in immature cells and detects longer myofibrils in mature samples.

Significance. If the central detection-accuracy claim were properly validated, this would be a valuable contribution to cardiac image analysis: it addresses an acknowledged limitation of the original SarcGraph, works on single images, extracts per-sarcomere features, and is open-source. The held-out test correlations and the explainable clustering results are encouraging evidence that the extracted features carry predictive signal. However, the current evidence does not establish that the pipeline detects more true structures rather than fewer structures overall; the missing ground-truth detection metrics are essential to assessing the practical value of the method.

major comments (4)
  1. [4.1, Abstract] The claim that the modified pipeline 'significantly reduced false positive sarcomere detections' is not validated against ground truth. The evidence is an aggregate drop in sarcomere counts in low-score cells (Figure 4A-i) and visual examples (Figure 4B). A reduction in detected counts is equally consistent with a loss of true detections, especially because the z-disc threshold of 0.3 (Section 3.3.1), the rule-based pruning, and the removal of single-sarcomere myofibrils (Section 3.4.2) can all suppress true positives. The paper reports no precision, recall, or F1 comparison against manual annotations of z-discs or sarcomeres, and no sensitivity analysis for the 0.3 threshold. Without such metrics, the headline improvement is unverified.
  2. [3.2, 3.3.1] The deep learning z-disc classifier is a central component, but its classification performance is never reported: there is no precision, recall, AUC, or confusion matrix on the manually labeled validation set. The 0.3 probability threshold and the other hand-set parameters (7-pixel distance, 22.5 degree angle, 60% intensity ratio) are introduced without justification or sensitivity analysis. Since all downstream features inherit the classifier's errors, the reader cannot determine whether the reported correlations reflect accurate detection or merely threshold-induced sparsity.
  3. [4.3, Conclusion] The paper itself notes in Section 4.3 that 'the Otsu thresholding method occasionally fails to detect z-discs that are visible to expert observers due to variations in image brightness.' Because the deep learning classifier operates only on contours found by Otsu thresholding, this is a recall ceiling that affects every downstream feature. The authors should quantify how frequently Otsu misses visible z-discs, e.g., by comparing Otsu contours against manual annotations on a subsample, and discuss how the missed detections affect the SVR and clustering results. The current claim that the pipeline is more accurate is not supported while this ceiling is uncharacterized.
  4. [4.3, Table 1] The statistical evidence for the supervised model is incomplete: the SVR hyperparameters (C=10, gamma=0.1) were selected on the Train set, so the Train correlation of 0.85 is not a predictive assessment, and the held-out test correlations are reported without confidence intervals, error bars, or significance tests. In addition, the comparison between SVR and decision-tree clustering in Table 1 treats the latter as a scoring method, but the cluster labels are evaluated against the same expert scores used to choose k and the feature thresholds; the claim that the unsupervised approach 'avoids the bias of expert-assigned organization scores' is therefore only partially true.
minor comments (5)
  1. [Abstract] The sentence 'These modification significantly reduced...' contains a grammatical error; 'modification' should be plural.
  2. [3.2.1] The phrase 'processing contour’s cropped region' is awkward; consider 'the cropped region centered on the contour being processed.'
  3. [Eq. (1)] The indicator function notation '1_{lmin < li < lmax}' is not previously defined; please define it explicitly.
  4. [4.1] There is a typo in the text: 'false positive sarcomere dection' should be 'detection.'
  5. [4.4, Figure 7] The transformation of continuous SVR scores into three categories uses thresholds at 2.33 and 3.67 without stating the rationale; please justify these boundaries.

Circularity Check

1 steps flagged · score 6.0 of 10

Longer-myofibril improvement is demonstrated by a post-processing rule that by construction extends myofibrils and removes single-sarcomere myofibrils; the held-out score predictions are otherwise non-circular.

  1. self definitional [Section 3.4.2 (Post-Processing Myofibril Extension) and Section 4.1 (Evaluating Improvements)]
    "we extend both ends of each detected myofibril by adding virtual z-discs... If the extension satisfies all rules, we add the z-disc to the graph as a new node and include the corresponding edge as a valid sarcomere... Finally, we remove any myofibrils that contain only a single sarcomere... increased the average myofibril length from 4.94 to 5.64 sarcomeres, demonstrating the effectiveness of the approach in recovering previously disconnected structures."

    The post-processing step is defined to add virtual z-discs/sarcomeres to myofibril ends and to delete every myofibril with only one sarcomere. The reported increase in average myofibril length (4.94 to 5.64) is therefore a direct mathematical consequence of these rules, not an independent measurement that previously disconnected structures were recovered. Section 4.1 then uses these longer myofibrils as evidence that the modified pipeline is able to better detect long myofibril chains, so the improvement claim reduces to the construction rule. No ground-truth myofibril annotations are used to verify that the added virtual sarcomeres correspond to real structures.

full rationale

Aside from the myofibril-length demonstration, the paper's derivations are not circular: the z-disc classifier is trained on 6,000 manually labeled contours and then applied to contours from held-out Test FISH and Test Live cells; the SVR is trained on the Train expert scores and evaluated on the two test sets (Pearson 0.77 and 0.79); the explainable clustering tree is fit on Train and applied to test data. Self-citations to the original SarcGraph [41,69] are used only as a baseline and for algorithm details, not as a load-bearing uniqueness argument. The separate claim that the modified pipeline significantly reduced false positive sarcomere detections is not circular but is unvalidated: it is inferred from lower sarcomere counts in low-score cells rather than from precision/recall against annotated z-discs, and the paper itself notes Otsu thresholding can miss z-discs visible to experts. Because one of the two headline improvements (longer myofibrils) is demonstrated by a metric that the post-processing rules force by construction, the circularity score is 6 rather than 0.

Assumptions & free parameters 8 free parameters · 4 assumptions · 1 invented entities

The central claims rest on a chain of hand-set thresholds, manual labels, and the assumption that Otsu contours contain all true z-discs. The no-free-lunch count is dominated by detection-pipeline hyperparameters rather than physical parameters.

free parameters (8)
  • Z-disc contour filtering probability threshold = 0.3
    Contours with predicted z-disc probability below 0.3 are discarded in Section 3.3.1; changing it directly changes sarcomere counts and all downstream features.
  • Z-disc ratio feature threshold = 0.5
    Section 3.5.1 defines the z-disc classification ratio using contours with probability > 0.5; this threshold determines a key input to the clustering tree.
  • Secondary z-disc intensity threshold = 60% of primary peak
    Section 3.3.2 treats a second intensity peak as a z-disc only if it is at least 60% of the primary peak; controls how often contours are split into multiple z-discs.
  • Myofibril extension thresholds = distance <= 7 px, new sarcomere length 10-20 px, angle < 22.5 degrees
    Rules in Section 3.4.2 for adding virtual z-discs; these choices determine myofibril length improvements (e.g., 4.94 to 5.64 sarcomeres).
  • Global alignment messaging parameters = angle < 30 degrees, eta = 0.8, T = 6
    Parameters in Appendix B control the global myofibril alignment score used in the ensemble; no sensitivity analysis is reported.
  • Ensemble prior pi0 = 0.5
    Section 3.4.1 sets no prior preference for edge being a sarcomere; the product formula's behavior depends on this value.
  • SVR hyperparameters = C=10, gamma=0.1, epsilon=0.5
    Fixed in Section 3.5.2 without a reported search; affect the reported Pearson correlations.
  • ExKMC cluster number k = 3
    Section 3.5.3 chooses three organization levels; the comparison in Section 4.4 is sensitive to this grouping.
assumptions (4)
  • domain assumption Otsu thresholding followed by marching squares detects all candidate z-disc contours; the deep-learning classifier only filters this candidate set.
    Invoked in Sections 3.1 and 3.2; if a z-disc is not contoured, it cannot be recovered, and the authors admit Otsu fails on some visible z-discs in Section 4.3.
  • domain assumption Manual z-disc labels on about 6,000 contour crops are correct and representative, with no reported inter-annotator agreement.
    Section 3.2.1; the entire supervised classifier and all downstream features depend on these labels.
  • domain assumption Expert organization scores with ICC=0.68 are a valid target for evaluating predictions despite acknowledged rater bias.
    Sections 2.1 and 4.2; the paper uses expert scores as ground truth for performance metrics while also arguing they are biased.
  • domain assumption The four graph-scoring methods are conditionally independent, justifying the probabilistic ensemble averaging product formula (Eq. 4).
    Section 3.4.1; no evidence for independence is provided, and correlations between scores would bias the ensemble.
invented entities (1)
  • Virtual z-discs (algorithmic nodes)
    purpose: Inserted at myofibril ends during post-processing to bridge breaks caused by missed or crossing z-discs.
    Section 3.4.2: these nodes do not correspond to detected image structures; the pipeline treats them as real z-discs in downstream metrics, adding a modeling layer not grounded in observed data.

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Pith. "Pith review of Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph." pith.science (2026). https://pith.science/paper/QD6NCRCH

@misc{pith2026250118714,
  author       = {Pith},
  title        = {Pith review of: Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QD6NCRCH}},
  note         = {Machine review of arXiv:2501.18714}
}
read the original abstract

In cardiac cells, structural organization is an important indicator of cell maturity and healthy function. Healthy cardiomyocytes exhibit well-aligned morphology with densely packed and organized sarcomeres. Immature or diseased cardiomyocytes typically lack this organized structure. Critically, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer a valuable model for studying human cardiac cells in a controlled environment. However, these cells often exhibit a disorganized structure. In this work, we extend the SarcGraph computational framework -- designed to assess the structural and functional behavior of hiPSC-CMs -- to better accommodate the structural features of immature cells. There are two key enhancements: (1) incorporating a deep learning-based z-disc classifier, and (2) introducing a novel ensemble graph-scoring approach. These modification significantly reduced false positive sarcomere detections in immature cells, and resulted in the detection of longer myofibrils in mature samples. With this enhanced framework, we analyze an open-source dataset published by the Allen Institute for Cell Science, where, for the first time, we are able to extract key structural features from these data using information from each individually detected sarcomere. Not only are we able to use these structural features to predict expert scores, but we are also able to use these structural features to identify bias in expert scoring and offer an alternative unsupervised learning approach based on explainable clustering. These results demonstrate the efficacy of our modified SarcGraph in extracting biologically meaningful features, enabling a deeper understanding of hiPSC-CM structural integrity. By making our code and tools open-source, we aim to empower the broader cardiac research community and foster further development of computational tools for cardiac tissue analysis.

Figures

Figures reproduced from arXiv: 2501.18714 by the authors.

Figure 1
Figure 1. Schematic representation of selected data from the Allen Institute dataset. Note that the original dataset contains additional channels and information not depicted here. Panels show: (A-i) a Field of View (FoV) from a representative RNA FISH well displaying multiple cells with segmentation overlays, (A-ii) a single cell segmented from the field of view image (highlighted in yellow), and (A-iii) detected contours of… view at source ↗
Figure 2
Figure 2. Enhancements to SarcGraph’s z-disc segmentation pipeline. (A) Starting from a raw cell image with all detected contours shown in blue, each contour is processed individually to crop a 128 × 128 pixel region centered on it (current processing contour highlighted in yellow). This region serves as the image representation of the processing contour and is transformed into two distinct input types: Type 1, which preserve… view at source ↗
Figure 3
Figure 3. The modified SarcGraph pipeline. (A) Schematically illustrates SarcGraph taking a raw image of a cell as input and, in two steps — z-disc segmentation and sarcomere detection — outputs a list of detected sarcomeres, visualized as red lines with light blue dots indicating z-discs. (B) Visualizes the modified z-disc segmentation step in SarcGraph, where (1) shows the original Otsu-thresholding-based contour detection,… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Here we compare the performance of the original and modified SarcGraph software for detecting sarcomeres. (A) Violin plots comparing sarcomere count and myofibril length (sarcomeres per myofibril) across samples from the Train set, grouped by cell organization score (l…
Figure 5
Figure 5. Figure 5: This figure highlights the limitations of manual scoring. (A) Cells arranged by sarcomere count normalized by cell area (ranked) within each score group (1 to 5), highlighting variation within score groups and visual similarity between cells from adjacent groups. (B) C…
Figure 6
Figure 6. Figure 6: This figure presents the performance of a Support Vector Regression (SVR) model on predicting cellular [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Application of explainable clustering on cell features for unsupervised organization scoring. (A-i) Decision tree [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

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Reference graph

Works this paper leans on

69 extracted references · 61 canonical work pages

  1. [1]

    Programming cellular alignment in engineered cardiac tissue via bioprinting anisotropic organ building blocks

    John H Ahrens, Sebastien GM Uzel, Mark Skylar-Scott, Mariana M Mata, Aric Lu, Katharina T Kroll, and Jennifer A Lewis. Programming cellular alignment in engineered cardiac tissue via bioprinting anisotropic organ building blocks. Advanced Materials, 34(26):2200217, 2022

    work page 2022

  2. [2]

    Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo µct imaging and deep learning

    Pouyan Asgharzadeh, Oliver Röhrle, Bettina M Willie, and Annette I Birkhold. Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo µct imaging and deep learning. Acta Biomaterialia, 106: 193–207, 2020

    work page 2020

  3. [3]

    Sarcomere alignment is regulated by myocyte shape

    Mark-Anthony Bray, Sean P Sheehy, and Kevin Kit Parker. Sarcomere alignment is regulated by myocyte shape. Cell motility and the cytoskeleton, 65(8):641–651, 2008

    work page 2008

  4. [4]

    Measurement and analysis of sarcomere length in rat cardiomyocytes in situ and in vitro.American Journal of Physiology-Heart and Circulatory Physiology, 298(5):H1616–H1625, 2010

    Gil Bub, Patrizia Camelliti, Christian Bollensdorff, Daniel J Stuckey, Graham Picton, RAB Burton, Kieran Clarke, and Peter Kohl. Measurement and analysis of sarcomere length in rat cardiomyocytes in situ and in vitro.American Journal of Physiology-Heart and Circulatory Physiology, 298(5):H1616–H1625, 2010

    work page 2010

  5. [5]

    Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes

    Lu Cao, Andries D van der Meer, Fons J Verbeek, and Robert Passier. Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes. BMC bioinformatics, 21:1–12, 2020

    work page 2020

  6. [6]

    Automated sarcomere structure analysis for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes

    Lu Cao, Linde Schoenmaker, Simone A Ten Den, Robert Passier, Verena Schwach, and Fons J Verbeek. Automated sarcomere structure analysis for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes. Microscopy and Microanalysis, 29(1):254–264, 2023

    work page 2023

  7. [7]

    Cellprofiler: image analysis software for identifying and quantifying cell phenotypes

    Anne E Carpenter, Thouis R Jones, Michael R Lamprecht, Colin Clarke, In Han Kang, Ola Friman, David A Guertin, Joo Han Chang, Robert A Lindquist, Jason Moffat, et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome biology, 7:1–11, 2006

    work page 2006

  8. [8]

    Deep learning for cardiac image segmentation: a review

    Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, and Daniel Rueckert. Deep learning for cardiac image segmentation: a review. Frontiers in cardiovascular medicine, 7:25, 2020

    work page 2020

Show all 69 references
  1. [9]

    A simple framework for contrastive learning of visual representations

    Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In Hal Daumé III and Aarti Singh, editors,Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings o...

    2020

  2. [10]

    Multimodal object detection via probabilistic ensembling

    Yi-Ting Chen, Jinghao Shi, Zelin Ye, Christoph Mertz, Deva Ramanan, and Shu Kong. Multimodal object detection via probabilistic ensembling. In European Conference on Computer Vision, pages 139–158. Springer, 2022

    2022

  3. [11]

    Multiscale characteri- zation of heart failure

    F Sahli Costabal, JS Choy, Kevin L Sack, Julius M Guccione, GS Kassab, and Ellen Kuhl. Multiscale characteri- zation of heart failure. Acta biomaterialia, 86:66–76, 2019

    2019

  4. [12]

    Cardiac sarcomere mechanics in health and disease

    Claudia Crocini and Michael Gotthardt. Cardiac sarcomere mechanics in health and disease. Biophysical reviews, 13(5):637–652, 2021

    2021

  5. [13]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    2009

  6. [14]

    Machine learning identifies remodeling patterns in human lung extracellular matrix

    Monica J Emerson, Oliver Willacy, Chris D Madsen, Raphael Reuten, Christian B Brøchner, Thomas K Lund, Anders B Dahl, Thomas HL Jensen, Janine T Erler, and Alejandro E Mayorca-Guiliani. Machine learning identifies remodeling patterns in human lung extracellular matrix. Acta Bi...

    2024

  7. [15]

    A density-based algorithm for discovering clusters in large spatial databases with noise

    Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, volume 96, pages 226–231, 1996

    1996

  8. [16]

    Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges

    Jourdan K Ewoldt, Samuel J DePalma, Maggie E Jewett, M Ça˘gatay Karakan, Yih-Mei Lin, Paria Mir Hashemian, Xining Gao, Lihua Lou, Micheal A McLellan, Jonathan Tabares, et al. Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing...

    2024

  9. [17]

    Serial dependence in visual perception

    Jason Fischer and David Whitney. Serial dependence in visual perception. Nature neuroscience, 17(5):738–743, 2014

    2014

  10. [18]

    Exkmc: Expanding explainable k-means clustering

    Nave Frost, Michal Moshkovitz, and Cyrus Rashtchian. Exkmc: Expanding explainable k-means clustering. arXiv preprint arXiv:2006.02399, 2020

    2006 arXiv

  11. [19]

    K. A. Gerbin, T. Grancharova, R. M. Donovan-Maiye, M. C. Hendershott, H. G. Anderson, J. M. Brown, J. Chen, S. Q. Dinh, J. L. Gehring, G. R. Johnson, H. Lee, A. Nath, A. M. Nelson, M. F. Sluzewski, M. P. Viana, C. Yan, R. J. Zaunbrecher, K. R. Cordes Metzler, N. Gaudreault, T....

    2021

  12. [20]

    Cell states beyond transcriptomics: Integrating structural organization and gene expression in hipsc-derived cardiomyocytes

    Kaytlyn A Gerbin, Tanya Grancharova, Rory M Donovan-Maiye, Melissa C Hendershott, Helen G Anderson, Jackson M Brown, Jianxu Chen, Stephanie Q Dinh, Jamie L Gehring, Gregory R Johnson, et al. Cell states beyond transcriptomics: Integrating structural organization and gene expre...

    2021

  13. [21]

    Self-organization of muscle cell structure and function

    Anna Grosberg, Po-Ling Kuo, Chin-Lin Guo, Nicholas A Geisse, Mark-Anthony Bray, William J Adams, Sean P Sheehy, and Kevin Kit Parker. Self-organization of muscle cell structure and function. PLoS computational biology, 7(2):e1001088, 2011

    2011

  14. [22]

    Statistical and structural approaches to texture

    Robert M Haralick. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786–804, 1979

    1979

  15. [23]

    A data-driven computational model for engineered cardiac microtissues

    Javiera Jilberto, Samuel J DePalma, Jason Lo, Hiba Kobeissi, Lani Quach, Emma Lejeune, Brendon M Baker, and David Nordsletten. A data-driven computational model for engineered cardiac microtissues. Acta biomaterialia, 172:123–134, 2023

    2023

  16. [24]

    Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle

    Sotirios Kakaletsis, William D Meador, Mrudang Mathur, Gabriella P Sugerman, Tomasz Jazwiec, Marcin Malinowski, Emma Lejeune, Tomasz A Timek, and Manuel K Rausch. Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the l...

    2021

  17. [25]

    Self-supervised deep learning encodes high-resolution features of protein subcellular localization

    Hirofumi Kobayashi, Keith C Cheveralls, Manuel D Leonetti, and Loic A Royer. Self-supervised deep learning encodes high-resolution features of protein subcellular localization. Nature methods, 19(8):995–1003, 2022

    2022

  18. [26]

    Structural immaturity of human ipsc-derived cardiomyocytes: in silico investigation of effects on function and disease modeling

    Jussi T Koivumäki, Nikolay Naumenko, Tomi Tuomainen, Jouni Takalo, Minna Oksanen, Katja A Puttonen, Šárka Lehtonen, Johanna Kuusisto, Markku Laakso, Jari Koistinaho, et al. Structural immaturity of human ipsc-derived cardiomyocytes: in silico investigation of effects on functi...

    2018

  19. [27]

    A generalized laplacian of gaussian filter for blob detection and its applications

    Hui Kong, Hatice Cinar Akakin, and Sanjay E Sarma. A generalized laplacian of gaussian filter for blob detection and its applications. IEEE transactions on cybernetics, 43(6):1719–1733, 2013

    2013

  20. [28]

    A guideline of selecting and reporting intraclass correlation coefficients for reliability research

    Terry K Koo and Mae Y Li. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2):155–163, 2016

    2016

  21. [29]

    Sarcnet: A novel ai-based framework to automatically analyze and score sarcomere organizations in fluorescently tagged hipsc-cms

    Huyen Le, Khiet Dang, Tien Lai, Nhung Nguyen, Mai Tran, and Hieu Pham. Sarcnet: A novel ai-based framework to automatically analyze and score sarcomere organizations in fluorescently tagged hipsc-cms. arXiv preprint arXiv:2405.17926, 2024

    2024 arXiv

  22. [30]

    Rater effects on essay scoring: A multilevel analysis of severity drift, central tendency, and rater experience

    George Leckie and Jo-Anne Baird. Rater effects on essay scoring: A multilevel analysis of severity drift, central tendency, and rater experience. Journal of Educational Measurement, 48(4):399–418, 2011

    2011

  23. [31]

    A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images

    Liang Liang, Minliang Liu, and Wei Sun. A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images. Acta biomaterialia, 63:227–235, 2017

    2017

  24. [32]

    Human induced pluripotent stem cell for modeling cardiovascular diseases

    Ping Liang and Jie Du. Human induced pluripotent stem cell for modeling cardiovascular diseases. Regenerative medicine research, 2:1–3, 2014

    2014

  25. [33]

    Drug screening using a library of human induced pluripotent stem cell–derived cardiomyocytes reveals disease-specific patterns of cardiotoxicity

    Ping Liang, Feng Lan, Andrew S Lee, Tingyu Gong, Veronica Sanchez-Freire, Yongming Wang, Sebastian Diecke, Karim Sallam, Joshua W Knowles, Paul J Wang, et al. Drug screening using a library of human induced pluripotent stem cell–derived cardiomyocytes reveals disease-specific ...

    2013

  26. [34]

    A survey on deep learning in medical image analysis

    Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez. A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88, 2017

    2017

  27. [35]

    Least squares quantization in pcm

    Stuart Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129–137, 1982

    1982

  28. [36]

    Lorensen and Harvey E

    William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH Comput. Graph., 21(4):163–169, aug 1987. ISSN 0097-8930. doi:10.1145/37402.37422. URL https://doi.org/10.1145/37402.37422

    1987

  29. [37]

    The vertebrate muscle z-disc: sarcomere anchor for structure and signalling

    Pradeep K Luther. The vertebrate muscle z-disc: sarcomere anchor for structure and signalling. Journal of muscle research and cell motility, 30:171–185, 2009. 29 arXiv Template A PREPRINT

    2009

  30. [38]

    Theory of edge detection

    David Marr and Ellen Hildreth. Theory of edge detection. Proceedings of the Royal Society of London. Series B. Biological Sciences, 207(1167):187–217, 1980

    1980

  31. [39]

    2024 heart disease and stroke statistics: a report of us and global data from the american heart association

    Seth S Martin, Aaron W Aday, Zaid I Almarzooq, Cheryl AM Anderson, Pankaj Arora, Christy L Avery, Carissa M Baker-Smith, Bethany Barone Gibbs, Andrea Z Beaton, Amelia K Boehme, et al. 2024 heart disease and stroke statistics: a report of us and global data from the american he...

    2024

  32. [40]

    Transcriptome profiling of patient-specific human ipsc- cardiomyocytes predicts individual drug safety and efficacy responses in vitro

    Elena Matsa, Paul W Burridge, Kun-Hsing Yu, John H Ahrens, Vittavat Termglinchan, Haodi Wu, Chun Liu, Praveen Shukla, Nazish Sayed, Jared M Churko, et al. Transcriptome profiling of patient-specific human ipsc- cardiomyocytes predicts individual drug safety and efficacy respon...

    2016

  33. [41]

    Sarcgraph: A python package for analyzing the contractile behavior of pluripotent stem cell-derived cardiomyocytes

    Saeed Mohammadzadeh and Emma Lejeune. Sarcgraph: A python package for analyzing the contractile behavior of pluripotent stem cell-derived cardiomyocytes. Journal of Open Source Software, 8(85):5322, 2023

    2023

  34. [42]

    Investigating deep learning model calibration for classification problems in mechanics

    Saeed Mohammadzadeh, Peerasait Prachaseree, and Emma Lejeune. Investigating deep learning model calibration for classification problems in mechanics. Mechanics of Materials, 184:104749, 2023

    2023

  35. [43]

    Striated myocyte structural integrity: Automated analysis of sarcomeric z-discs

    Tessa Altair Morris, Jasmine Naik, Kirby Sinclair Fibben, Xiangduo Kong, Tohru Kiyono, Kyoko Yokomori, and Anna Grosberg. Striated myocyte structural integrity: Automated analysis of sarcomeric z-discs. PLoS computational biology, 16(3):e1007676, 2020

    2020

  36. [44]

    Explainable k-means and k-medians clustering

    Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, and Nave Frost. Explainable k-means and k-medians clustering. In International conference on machine learning, pages 7055–7065. PMLR, 2020

    2020

  37. [45]

    Algorithms for hierarchical clustering: an overview, ii.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6):e1219, 2017

    Fionn Murtagh and Pedro Contreras. Algorithms for hierarchical clustering: an overview, ii.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6):e1219, 2017

    2017

  38. [46]

    Derivation of human induced pluripotent stem cells for cardiovascular disease modeling

    Kamileh Narsinh, Kazim H Narsinh, and Joseph C Wu. Derivation of human induced pluripotent stem cells for cardiovascular disease modeling. Circulation Research, 108(9):1146–1156, 2011

    2011

  39. [47]

    Independent regulation of z-lines and m-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcapp

    Abigail C Neininger-Castro, James B Hayes, Zachary C Sanchez, Nilay Taneja, Aidan M Fenix, Satish Moparthi, Stéphane Vassilopoulos, and Dylan Tyler Burnette. Independent regulation of z-lines and m-lines during sarcomere assembly in cardiac myocytes revealed by the automatic i...

    2023

  40. [48]

    Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly.Circulation, 105(4):446–451, 2002

    Hideshi Niimura, Kristen K Patton, William J McKenna, Johann Soults, Barry J Maron, JG Seidman, and Christine E Seidman. Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly.Circulation, 105(4):446–451, 2002

    2002

  41. [49]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  42. [50]

    A threshold selection method from gray-level histograms

    Nobuyuki Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66, 1979. doi:10.1109/TSMC.1979.4310076

    1979

  43. [51]

    Tracking single hipsc-derived cardiomyocyte contractile function using contrax an efficient pipeline for traction force measurement

    Gaspard Pardon, Alison S Vander Roest, Orlando Chirikian, Foster Birnbaum, Henry Lewis, Erica A Castillo, Robin Wilson, Aleksandra K Denisin, Cheavar A Blair, Colin Holbrook, et al. Tracking single hipsc-derived cardiomyocyte contractile function using contrax an efficient pip...

    2024

  44. [52]

    Sarcoptim for imagej: high-frequency online sarcomere length computing on stimulated cardiomyocytes.American Journal of Physiology-Cell Physiology, 311(2):C277–C283, 2016

    Côme Pasqualin, François Gannier, Angèle Yu, Claire O Malécot, Pierre Bredeloux, and Véronique Maupoil. Sarcoptim for imagej: high-frequency online sarcomere length computing on stimulated cardiomyocytes.American Journal of Physiology-Cell Physiology, 311(2):C277–C283, 2016

    2016

  45. [53]

    Structural phenotyping of stem cell-derived cardiomyocytes

    Francesco Silvio Pasqualini, Sean Paul Sheehy, Ashutosh Agarwal, Yvonne Aratyn-Schaus, and Kevin Kit Parker. Structural phenotyping of stem cell-derived cardiomyocytes. Stem cell reports, 4(3):340–347, 2015

    2015

  46. [54]

    Multi-imaging method to assay the contractile mechanical output of micropatterned human ipsc-derived cardiac myocytes

    Alexandre JS Ribeiro, Olivier Schwab, Mohammad A Mandegar, Yen-Sin Ang, Bruce R Conklin, Deepak Srivastava, and Beth L Pruitt. Multi-imaging method to assay the contractile mechanical output of micropatterned human ipsc-derived cardiac myocytes. Circulation research, 120(10):1...

    2017

  47. [55]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part ...

    2015

  48. [56]

    Modeling cardiovascular diseases with hipsc-derived cardiomyocytes in 2d and 3d cultures

    Claudia Sacchetto, Libero Vitiello, Leon J de Windt, Alessandra Rampazzo, and Martina Calore. Modeling cardiovascular diseases with hipsc-derived cardiomyocytes in 2d and 3d cultures. International journal of molecular sciences, 21(9):3404, 2020. 30 arXiv Template A PREPRINT

    2020

  49. [57]

    A survey of decision tree classifier methodology

    S Rasoul Safavian and David Landgrebe. A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3):660–674, 1991

    1991

  50. [58]

    Quality metrics for stem cell-derived cardiac myocytes

    Sean P Sheehy, Francesco Pasqualini, Anna Grosberg, Sung Jin Park, Yvonne Aratyn-Schaus, and Kevin Kit Parker. Quality metrics for stem cell-derived cardiac myocytes. Stem cell reports, 2(3):282–294, 2014

    2014

  51. [59]

    Induced pluripotent stem cell technology: a decade of progress

    Yanhong Shi, Haruhisa Inoue, Joseph C Wu, and Shinya Yamanaka. Induced pluripotent stem cell technology: a decade of progress. Nature reviews Drug discovery, 16(2):115–130, 2017

    2017

  52. [60]

    Intraclass correlations: uses in assessing rater reliability

    E Shrout Patrick and L Fleiss Joseph. Intraclass correlations: uses in assessing rater reliability. Psychological bulletin, 86(2):420–28, 1979

    1979

  53. [61]

    Harding, Neda Mohammadi, Valeria V Orlova, Milena Bellin, Christine L Mummery, and Berend J van Meer

    Jeroen M Stein, Ulgu Arslan, Marnix Franken, Jessica C de Greef, Sian E. Harding, Neda Mohammadi, Valeria V Orlova, Milena Bellin, Christine L Mummery, and Berend J van Meer. Software tool for automatic quantification of sarcomere length and organization in fixed and live 2d a...

    2022

  54. [62]

    High content analysis identifies unique morphological features of reprogrammed cardiomyocytes

    Matthew D Sutcliffe, Philip M Tan, Antonio Fernandez-Perez, Young-Jae Nam, Nikhil V Munshi, and Jeffrey J Saucerman. High content analysis identifies unique morphological features of reprogrammed cardiomyocytes. Scientific reports, 8(1):1258, 2018

    2018

  55. [63]

    Efficientnetv2: Smaller models and faster training

    Mingxing Tan and Quoc Le. Efficientnetv2: Smaller models and faster training. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 10096–10106. PMLR, 18–24 J...

    2021

  56. [64]

    Toepfer, Arun Sharma, Marcelo Cicconet, Amanda C

    Christopher N. Toepfer, Arun Sharma, Marcelo Cicconet, Amanda C. Garfinkel, Michael Mücke, Meraj Neyazi, Jon A.L. Willcox, Radhika Agarwal, Manuel Schmid, Jyoti Rao, Jourdan Ewoldt, Olivier Pourquié, Anant Chopra, Christopher S. Chen, Jonathan G. Seidman, and Christine E. Seid...

    2019 doi

  57. [65]

    Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D

    Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, and the scikit-image contributors. scikit-image: image processing in Python. PeerJ, 2:e453, 6 2014. ISSN 2167-8359. doi:10.7717/pe...

    2014 doi

  58. [66]

    A survey on semi-supervised learning

    Jesse E van Engelen and Holger H Hoos. A survey on semi-supervised learning. Machine Learning, 109(2): 373–440, 2020

    2020

  59. [67]

    Modelling inherited cardiac disease using human induced pluripotent stem cell-derived cardiomyocytes: progress, pitfalls, and potential

    Alain van Mil, Geerthe Margriet Balk, Klaus Neef, Jan Willem Buikema, Folkert W Asselbergs, Sean M Wu, Pieter A Doevendans, and Joost PG Sluijter. Modelling inherited cardiac disease using human induced pluripotent stem cell-derived cardiomyocytes: progress, pitfalls, and pote...

    2018

  60. [68]

    S4l: Self-supervised semi-supervised learning

    Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer. S4l: Self-supervised semi-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019

    2019

  61. [69]

    Sarc-graph: Automated segmentation, tracking, and analysis of sarcomeres in hipsc-derived cardiomyocytes

    Bill Zhao, Kehan Zhang, Christopher S Chen, and Emma Lejeune. Sarc-graph: Automated segmentation, tracking, and analysis of sarcomeres in hipsc-derived cardiomyocytes. PLoS computational biology, 17(10):e1009443, 2021. 31

    2021

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.