Abstract
In the field of digital pathology, accurate and rapid segmentation of pathology images are critical for cancer diagnosis and prognosis. This paper presents SegPath-YOLO, a novel tool in digital pathology for nuclei segmentation and tumor microenvironment feature extraction tool, leveraging the robustness of YOLO series with significant enhancements for accuracy and speed. SegPath-YOLO addresses critical challenges in pathology image analysis, particularly in handling overlapping and high-density cellular structures and ensuring rapid processing without sacrificing precision. The novelty of SegPath-YOLO lies in its Segmentation and Overlapping-Aware Loss, which utilizes a binary overlap mask to identify and enhance the loss in overlapping regions. In conjunction with PathNuclei attention mechanisms, SegPath-YOLO not only refines segmentation results but also contributes to a deeper characterization and quantification of the tumor microenvironment, significantly aiding in survival outcome predictions. The comprehensive evaluations demonstrate that SegPath-YOLO achieves superior performance compared to existing tools, including the YOLOv8 by effectively balancing computational efficiency with high accuracy. We validate SegPath-YOLO’s across two different tissue types, it demonstrates its potential as a high generalizability tool in digital pathology. Overall, SegPath-YOLO is a state of the art tool for pathologist and researcher, enabling the extraction of reliable and clinically relevant data from complex pathology image. The SegPath-YOLO can be acessed at https://github.com/yaober/SegPath-YOLO.