The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

• URL: http://arxiv.org/abs/2308.05864v2
• Date: Mon, 1 Apr 2024 16:11:58 GMT
• Title: The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
• Authors: Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, Wei Lou, Haofeng Li, Eric Upschulte, Timo Dickscheid, José Guilherme de Almeida, Yixin Wang, Lin Han, Xin Yang, Marco Labagnara, Vojislav Gligorovski, Maxime Scheder, Sahand Jamal Rahi, Carly Kempster, Alice Pollitt, Leon Espinosa, Tâm Mignot, Jan Moritz Middeke, Jan-Niklas Eckardt, Wangkai Li, Zhaoyang Li, Xiaochen Cai, Bizhe Bai, Noah F. Greenwald, David Van Valen, Erin Weisbart, Beth A. Cimini, Trevor Cheung, Oscar Brück, Gary D. Bader, Bo Wang,
• Abstract summary: This benchmark comprises over 1500 labeled images from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that exceeds existing methods. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
• Score: 26.613802004468578
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 multi-modality cell segmentation benchmark, comprising over 1500 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.

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