C1M2:a universal algorithm for 3D instance segmentation,annotation,and quantification of irregular cells  

作　　者：Hao Zheng Songlin Huang Jing Zhang Ren Zhang Jialu Wang Jing Yuan Anan Li Xin Yang Zhihong Zhang 

机构地区：[1]Britton Chance Center and MOE Key Laboratory for Biomedical Photonics,Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology,Wuhan 430074,China [2]Key Laboratory of Biomedical Engineering of Hainan Province,School of Biomedical Engineering,Hainan University,Haikou 570228,China [3]School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Science China(Life Sciences)》2023年第10期2415-2428,共14页中国科学（生命科学英文版）

基　　金：the National Key Research and Development Program of China(2017YFA0700403,2017YFA0700402);the National Natural Science Foundation of China(62061160490);the Applied Fundamental Research of Wuhan(2020010601012167);the Fundamental Research Funds for the Central Universities(2019kfy XMBZ022);the Innovation Fund of Wuhan National Laboratory for Optoelectronics(WNLO)。

摘　　要：Cell instance segmentation is a fundamental task for many biological applications,especially for packed cells in three-dimensional(3D)microscope images that can fully display cellular morphology.Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional(2D)instance segmentation.However,current methods cannot achieve high segmentation accuracy for irregular cells in 3D images.In this study,we introduce a universal,morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice(C1M2),which can segment cells from a wide range of image types and does not require nucleus images.C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells.Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.

关 键 词：3D instance segmentation irregular cells fluorescence images neural networks fluorescence intensity tissue cytometry 

分 类 号：Q2-33[生物学—细胞生物学]