A Comparative Evaluation of YOLO-Segmentation Models for Coronary Artery Segmentation on the ARCADE Dataset

Coronary X-ray angiography is the clinical reference for visualizing coronary anatomy, but automated instance-level segment labeling remains difficult because of low contrast, overlapping branches, class imbalance across 25 SYNTAX-defined segments, and heterogeneous published protocols. This protocol therefore provides a reproducible, image-level comparison of five You Only Look Once (YOLO) segmentation models-YOLOv8-seg, YOLOv9c-seg, YOLO11-seg, YOLO12-seg, and YOLO26-seg-on the public Roboflow v1 ARCADE release of 3,587 expert-annotated frames divided into 3,138 training, 299 validation, and 150 test frames. Models were initialized from official segmentation checkpoints, trained only on the training split with shared stochastic gradient descent (SGD) settings and validation-based early stopping and checkpoint selection, and evaluated once post hoc on the untouched test split; mask mean average precision (mAP) at intersection over union (IoU) thresholds of 0.50-0.95 (mAP@50-95) was the primary accuracy metric and A100 model-level latency at 32-bit floating-point precision (FP32) was a secondary efficiency descriptor. On validation, the larger YOLOv9c-seg obtained the highest observed mask mAP@50-95 of 0.210; on the test split, compact YOLO11-seg obtained the highest observed mask mAP@50-95 of 0.241, followed by YOLO26-seg (0.239) and YOLOv9c-seg (0.235). Major proximal segments were consistently easier to segment than rare distal variants, while recorded inference times of 0.6 ms versus 3.0 ms per frame describe model-level throughput rather than end-to-end clinical latency. These single-seed, image-level results do not establish patient-level independence, statistical superiority, or clinical utility; they are intended as a methodological benchmark that others can reimplement, with prospective, hardware-specific, and patient-level validation still required before any workflow claim.