文献类型： 外文期刊
作者： Liu, Ping 1 ; Li, Jun 1 ; Chang, Jiaxing 1 ; Hu, Pinli 2 ; Sun, Yue 2 ; Jiang, Yanan 2 ; Zhang, Fan 2 ; Shao, Haojing 2 ;
作者机构： 1.Taiyuan Univ Technol, Coll Comp Sci & Technol, Coll Data Sci, Jinzhong 030600, Peoples R China
2.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Shenzhen Branch, Guangdong Lab Lingnan Modern Agr,Minist Agr & Rura, 7 Pengfei Rd, Shenzhen 518120, Peoples R China
关键词： cell segmentation; image processing; 2D cell; performance
期刊名称：CELLS （ 影响因子：6.0； 五年影响因子：6.7 ）
ISSN：
年卷期： 2024 年 13 卷 4 期
页码：
收录情况： SCI
摘要： Cell segmentation is an important task in the field of image processing, widely used in the life sciences and medical fields. Traditional methods are mainly based on pixel intensity and spatial relationships, but have limitations. In recent years, machine learning and deep learning methods have been widely used, providing more-accurate and efficient solutions for cell segmentation. The effort to develop efficient and accurate segmentation software tools has been one of the major focal points in the field of cell segmentation for years. However, each software tool has unique characteristics and adaptations, and no universal cell-segmentation software can achieve perfect results. In this review, we used three publicly available datasets containing multiple 2D cell-imaging modalities. Common segmentation metrics were used to evaluate the performance of eight segmentation tools to compare their generality and, thus, find the best-performing tool.
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