The multimodality cell segmentation challenge: toward universal solutions

• Published in: Nature methods Vol. 21; no. 6; pp. 1103 - 1113
• Main Authors: Ma, Jun, Xie, Ronald, Ayyadhury, Shamini, Ge, Cheng, Gupta, Anubha, Gupta, Ritu, Gu, Song, Zhang, Yao, Lee, Gihun, Kim, Joonkee, Lou, Wei, Li, Haofeng, Upschulte, Eric, Dickscheid, Timo, de Almeida, José Guilherme, Wang, Yixin, Han, Lin, Yang, Xin, Labagnara, Marco, Gligorovski, Vojislav, Scheder, Maxime, Rahi, Sahand Jamal, Kempster, Carly, Pollitt, Alice, Espinosa, Leon, Mignot, Tâm, Middeke, Jan Moritz, Eckardt, Jan-Niklas, Li, Wangkai, Li, Zhaoyang, Cai, Xiaochen, Bai, Bizhe, Greenwald, Noah F, Van Valen, David, Weisbart, Erin, Cimini, Beth A, Cheung, Trevor, Brück, Oscar, Bader, Gary D, Wang, Bo
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.06.2024
• Subjects: Algorithms; Benchmarks; Biology; Data science; Datasets; Image analysis; Image processing; Image segmentation; Machine learning; Microscopy; Parameters; Segmentation; Teams; Transformers
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-024-02233-6

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.