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Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph

Fig 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 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 preserves the original pixel intensities, and Type 2, which removes pixel intensity dependence. (B) depicts our ensemble classification architecture, where the two input types are processed through separate classifiers: a SimCLR-based EfficientNetv2 trained on both labeled and unlabeled data (Classifier 1), and a DINO-v2-based feature extractor followed by an MLP trained only on labeled data (Classifier 2). The final Z-disc probability is computed as the average of both classifiers’ predictions. (C) shows the predicted Z-disc probabilities for all detected contours in the sample cell, where each contour is colored according to its predicted probability of being a Z-disc (higher values in warmer colors indicate higher probability of being a Z-disc). (D) illustrates the application of our z-disc location correction method to three contours selected from different samples. The yellow line highlights the processing contour, while blue lines outline all detected neighboring contours. The blue dot marks the original z-disc location marked by the original SarcGraph, and the red dots indicate the corrected positions. Note that this approach can both refine z-disc locations and identify multiple z-discs that were previously merged into a single contour; see Sect E and Fig A8 in S1 Appendix.

doi: https://doi.org/10.1371/journal.pcbi.1013436.g002