The field of medical image analysis is rapidly evolving, with a growing emphasis on incorporating anatomical knowledge and uncertainty quantification into image segmentation models. Recent developments have focused on integrating background knowledge into semantic segmentation models, leveraging vision-language models for reference-based anatomical understanding, and improving the accuracy of peripheral blood cell detection. Notably, the use of conditional random fields, logic tensor networks, and self-supervised learning techniques has shown promise in advancing the field.

Some noteworthy papers include: KG-SAM, which introduces a knowledge-guided framework for segmenting medical images with improved accuracy and reliability. RAU, which explores the capability of vision-language models for reference-based identification, localization, and segmentation of anatomical structures in medical images. Autoproof, which proposes an automated segmentation proofreading approach for connectomics, reducing manual annotation costs and increasing connectivity completion rates. MATCH, which presents a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features in histopathology image analysis.