Mazur Martyna, Krauze Wojciech
Warsaw University of Technology, 8 Boboli Str., Warsaw, 02-525, Poland.
Biomed Opt Express. 2023 Sep 5;14(10):5022-5035. doi: 10.1364/BOE.498275. eCollection 2023 Oct 1.
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.
通过光学衍射断层扫描（ODT）对生物细胞及其内部结构进行三维定量成像，是生物医学研究的重要组成部分。然而，对ODT图像进行定量分析需要高精度的三维分割，而现有的分割方法往往难以实现。因此，在这项工作中，我们提出了一种新的半自动方法，称为ODT-SAS，它结合了几种非机器学习技术来分割细胞及其两种细胞器：核仁和脂质结构（LS）。分别在细胞分割和细胞器分割方面，将ODT-SAS与Cellpose和逐片手动分割进行了比较。比较结果显示ODT-SAS优于Cellpose，并揭示了我们的技术在检测细胞、核仁和LS方面的潜力。
