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A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations

文献类型：期刊论文

作者Wu, Hao4,5; Niyogisubizo, Jovial3,4,5; Zhao, Keliang3,4,5; Meng, Jintao4,5; Xi, Wenhui4,5; Li, Hongchang2; Pan, Yi1; Wei, Yanjie4,5
刊名INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
出版日期2023-11-01
卷号24期号:22页码:28
关键词cell detection cell tracking deep learning weakly supervised learning iPS cell reprogramming brightfield microscopy
ISSN号1661-6596
DOI10.3390/ijms242216028
英文摘要The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model's performance improved as the quality of the labels used for training increased.
资助项目Key Research and Development Project of Guangdong Province
WOS研究方向Biochemistry & Molecular Biology ; Chemistry
语种英语
WOS记录号WOS:001115184600001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/38081]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wei, Yanjie
作者单位1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Coll Comp Sci & Control Engn, Shenzhen 518055, Peoples R China
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Biotechnol, Shenzhen 518055, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China
5.Chinese Acad Sci, Shenzhen Key Lab Intelligent Bioinformat, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Hao,Niyogisubizo, Jovial,Zhao, Keliang,et al. A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2023,24(22):28.
APA Wu, Hao.,Niyogisubizo, Jovial.,Zhao, Keliang.,Meng, Jintao.,Xi, Wenhui.,...&Wei, Yanjie.(2023).A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,24(22),28.
MLA Wu, Hao,et al."A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 24.22(2023):28.

入库方式： OAI收割

来源：计算技术研究所

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