Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, United States.
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae407.
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.
细胞分割是分析生物医学图像的基本任务。已经开发了许多用于细胞分割和实例分割的计算方法，但它们在各种情况下的性能还不是很清楚。我们系统地评估了 18 种分割方法在使用光学显微镜和荧光染色图像进行细胞核和整个细胞分割的性能。我们发现，结合注意力机制的通用方法表现出最佳的整体性能。我们确定了影响分割性能的各种因素，包括图像通道、训练数据的选择和细胞形态，并评估了方法在不同图像模态之间的通用性。我们还为在各种实际应用场景中选择最佳分割方法提供了指导。我们开发了 Seggal，这是一个在线资源，可下载已经使用各种组织和细胞类型进行预训练的分割模型，大大减少了训练细胞分割模型的时间和精力。
