Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph
Fig 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 (low: score < 3, medium: score = 3, high: score > 3). With the modified version of SarcGraph, low-score cells have near zero sarcomere count. Notably, across all score categories, average myofibril length is higher with the new SarcGraph. (B) Visual comparison of sarcomere detection in one representative cell from each expert score group (1–5), showing original SarcGraph (top) versus modified SarcGraph (bottom). In low-score cells, the original pipeline yields numerous false-positive sarcomeres, whereas the modified version correctly suppresses most of these spurious detections, producing near-zero counts. In medium- and high-score cells, the modified pipeline reveals longer, more continuous myofibril chains without altering the overall sarcomere count substantially. Note that while the modified SarcGraph pipeline significantly reduces false positives and improves detection, false positives and false negatives can still occur, indicating the model still has room for potential improvement.