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 methods across various segmentation tasks and imaging modalities. We also develop a benchmark test that covers both single-modal and multimodal methods. This test uses five diverse datasets, among which four are from conventional microscopy and one integrates sequencing with image data. Furthermore, it assesses seven algorithms based on three dimensions: effectiveness, robustness, and efficiency. Key findings indicate that deep learning models generally outperform traditional algorithms, with their advantage becoming more pronounced when image data is integrated with sequencing information.
Yang et al. (Thu,) studied this question.