Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning
A deeper understanding of the cellular and subcellular organization of tumor cells and their interactions with the tumor microenvironment will shed light on how cancer evolves and guide effective therapy choices. Electron microscopy (EM) images can provide detailed view of the cellular ultrastructure and are being generated at an ever-increasing rate. However, the bottleneck in their analysis is the delineation of the cellular structures to enable interpretable rendering. We have mitigated this limitation by using deep learning, specifically, the ResUNet architecture, to segment cells and subcellular ultrastructure. Our initial prototype focuses on segmenting nuclei and nucleoli in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. Trained with sparse manual labels, our method results in accurate segmentation of nuclei and nucleoli with best Dice score of 0.99 and 0.98 respectively. This method can be extended to other cellular structures, enabling deeper analysis of inter- and intracellular state and interactions.