arXiv:2606.16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U-Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical appl
This survey provides a timely review of the rapid advancements in medical image segmentation, driven by breakthroughs in AI architectures like U-Net, Transformer, and SAM.
Medical image segmentation is foundational for improving diagnostic accuracy, treatment planning efficiency, and disease monitoring, directly impacting healthcare outcomes and costs.
The systematic review and analysis of challenges and benchmarks will likely guide future research and development, accelerating the adoption of advanced AI in clinical settings.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Medical diagnostic companies
- · Traditional manual image analysis firms
- · Outdated diagnostic methods
Improved early detection and personalized treatment plans for various diseases due to enhanced image analysis.
Increased demand for specialized AI infrastructure and talent within the healthcare sector.
Shift in medical training curricula to emphasize AI-driven diagnostics and analysis, potentially leading to new specializations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI