Abstract 349: Segmentation Of Atherosclerotic Plaque Features From Histopathology Images Using Novel Deep Learning Techniques

Objective: Atherosclerotic plaques have a complex composition, consisting of inflammation, fibrosis, cholesterol crystals, hemorrhage, and/or calcification. The segmentation and quantification of plaque features in histopathology images form the foundation for studies evaluating plaque instability and the mechanisms that underlie the atherosclerotic process. Manual segmentation of plaque features from histology images is a tedious, time-consuming, and subjective visual recognition task. Herein, we present a fully automatic approach using state-of-the-art deep learning techniques to identify three major features of the atherosclerotic plaque: calcification, lipid core, and fibrosis. Methods: Plaques (n=70) were collected from patients who underwent a carotid endarterectomy at McGill University-affiliated hospitals. Hematoxylin and Eosin-stained sections were obtained from the region with the largest plaque burden. The “ground truth annotations” for lipid core, calcification, and fibrosis were performed manually by three blinded cardiovascular pathologists, using Sedeen Viewer. A total of 23,000 patches with 512x512 pixel size were extracted from our image dataset, and divided into train, validate, and test sets. Using Transfer Learning, multi-class U-Net models for semantic segmentation were trained on the patches to extract fibrosis, lipid, and calcification plaque features. Evaluation of model performance was based on the mean value of the Intersection over Union (Mean-IOU) between the prediction results and the “ground truth annotations”. Results: Our models resulted in an overall performance of 77% for test images, and a per-class performance for the three plaque features: fibrosis = 0.77±0.2, lipid core = 0.80±0.3, calcification = 0.75±0.25. However, a qualitative evaluation by the pathologists confirmed that the prediction results in fact outperformed the “ground truth annotations”, and detected non-annotated regions. Conclusion: To our knowledge, this is a first attempt at developing a fully automatic approach for atherosclerotic plaque feature segmentation from histology images. Our models can accelerate atherosclerosis research, by improving the speed, quality, and reproducibility of plaque analysis.