Artery segmentation and atherosclerotic plaque quantification using AI for murine whole slide images stained with oil red O
摘要
Atherosclerosis is the leading cause of death in Western industrial nations. To study the etiology of plaque progression, atherosclerotic mouse models are widely used. Traditionally, analyzing the obtained histological whole slide images of Oil Red O-stained aortic roots required manual segmentation. To accelerate this process, an artificial intelligence-driven solution is proposed that comprises three stages: (1) defining the region of interest (ROI) of the aortic root using a YOLOv8l object detector, (2) applying supervised machine learning with ensembles of U-Net++ networks for artery segmentation using ROI masks, and (3) performing plaque segmentation within arterial walls with the unsupervised W-Net method. To establish a robust segmentation pipeline, we benchmark our methods using manually created masks (