The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv:2308.05864v2 Announce Type: replace-cross
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 …

abstract analysis arxiv benchmark challenge cs.cv cs.lg eess.iv experimental images microscopy parameters q-bio.qm quantitative segmentation solutions type universal