3D Semantic Cell Segmentation via Propagation of 2D Results and Integration of Intercellular Priors

Accurate 3D semantic cell segmentation in volumetric electron microscopy images is crucial for analyzing tissue architecture and plays a key role in studying fundamental biological processes. While current 2D semantic segmentation methods benefiting from numerous pre-trained networks available in the literature are effective, extending these approaches to 3D segmentation remains challenging. This study introduces a novel automated approach for enhancing 3D semantic cell segmentation by propagating 2D cell delineations and integrating intercellular priors to constrain segmentation within cell boundaries. We demonstrate the efficacy of our approach for automated 3D semantic cell segmentation using patient-derived xenograft (PDX) hepatoblastoma tissue samples acquired by serial block-face scanning electron microscopy. The proposed method significantly improves 3D semantic cell segmentation, paving the way for bioarchitectural investigations at the cellular level using biological and pathological samples.