首页|CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

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Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development.In this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during C.elegans embryogenesis.Based on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house dataset.Experimental results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a～1.5-min interval and covers C.elegans embryogenesis from the 4-cell to 350-cell stages.The robustness of CShaperApp is also validated with the datasets from different laboratories.Furthermore,modularized implementation increases the flexibility in multi-task applications and pro-motes its flexibility for future enhancements.As cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data collection.The software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.

C.elegans embryogenesiscellular morphologycellular segmentationdeep learningdesktop software

Jianfeng Cao、Lihan Hu、Guoye Guan、Zelin Li、Zhongying Zhao、Chao Tang、Hong Yan

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Department of Electrical Engineering,City University of Hong Kong,Hong Kong,China

Centre for Intelligent Multidimensional Data Analysis Limited,Hong Kong,China

Department of Computer Science and Engineering,Chinese University of Hong Kong,Hong Kong,China

College of Computer and Information,Hohai University,Nanjing,China

Department of Computer Science,University of Iowa,Iowa City,Iowa,USA

Center for Quantitative Biology,Peking University,Beijing,China

Dana-Farber Cancer Institute and Department of Systems Biology,Harvard Medical School,Boston,Massachusetts,USA

Department of Biology,Hong Kong Baptist University,Hong Kong,China

State Key Laboratory of Environmental and Biological Analysis,Hong Kong Baptist University,Hong Kong,China

Peking-Tsinghua Center for Life Sciences,Peking University,Beijing,China

School of Physics,Peking University,Beijing,China

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2024

10.1002/qub2.47

定量生物学(英文版)

定量生物学(英文版)

ISSN：
年,卷(期)：2024.12(3)