Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images

Abstract

Three-dimensional, quantitative imaging of biological cells and their internal structures performed by optical diffraction tomography (ODT) is an important part of biomedical research. However, conducting quantitative analysis of ODT images requires performing 3D segmentation with high accuracy, often unattainable with available segmentation methods. Therefore, in this work, we present a new semi-automatic method, called ODT-SAS, which combines several non-machine-learning techniques to segment cells and 2 types of their organelles: nucleoli and lipid structures (LS). ODT-SAS has been compared with Cellpose and slice-by-slice manual segmentation, respectively, in cell segmentation and organelles segmentation. The comparison shows superiority of ODT-SAS over Cellpose and reveals the potential of our technique in detecting cells, nucleoli and LS.