SegPath-YOLO: A High-Speed, High-Accuracy Pathology Image Segmentation and Tumor Microenvironment Feature Extraction Tool

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.