Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph
Fig 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, (2) demonstrates the integration of our deep-learning-based z-disc classifier (Sect 3.3.1), and (3) depicts the effect of the procedural approach used to correct the location of detected z-discs (Sect 3.3.2). (C) Illustrates the modified sarcomere detection phase in SarcGraph, where (4) schematically shows the construction of a graph by connecting detected z-discs to their N nearest neighbors, followed by ensemble graph scoring with probabilistic ensemble averaging (Sect 3.4.1), (5) presents the results of graph pruning, and (6) highlights the effect of applying post-processing myofibril extension to obtain the final list of detected sarcomeres (Sect 3.4.2); see Sect F and Fig A9 in S1 Appendix.