학술논문
The multimodality cell segmentation challenge: toward universal solutions
Document Type
Article
Author
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
Source
Nature Methods; 20240101, Issue: Preprints p1-11, 11p
Subject
Language
ISSN
15487091; 15487105
Abstract
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.