Systematic evaluation of computational methods for cell segmentation.

ABSTRACT: Cell segmentation plays a crucial role in elucidating cell structure and function, understanding disease mechanisms, and aiding pathological diagnosis. Current surveys primarily categorize methods by their technical evolution stages, which may not fully capture the paradigm shift brought by deep learning. Moreover, their evaluation scope is largely confined to image-only approaches, overlooking the significant potential of multimodal data in enhancing cell/nucleus segmentation performance. Therefore, we propose a dual-dimensional classification framework for deep learning methods. It categorizes such methods into two types: task-oriented (e.g. semantic or instance segmentation) and data-oriented (e.g. single or multimodal inputs). Based on this, we systematically classify and summarize met

SUBMITTER: Yang R 

PROVIDER: S-EPMC12931453 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature