digital pathology; atherosclerosis; segmentation; staining; U-net; deeplabV3+

Atherosclerosis, a major cause of ischemic stroke worldwide, is characterized by plaque formation, particularly in the carotid bifurcation, leading to arterial stenosis. Traditional histology and light microscopy have been used to study atherosclerotic plaques, but the advent of digital pathology and artificial intelligence has provided new opportunities. In this work, we proposed an automatic segmentation method using convolutional neural networks (U-Net and DeepLabV3+) to delineate atherosclerotic carotid plaque tissue. The study included 835 images of histological slices stained with hematoxylin and eosin and Van Gieson's method from 114 patients. The results showed that DeepLabV3+ outperforms U-Net, achieving high accuracy for tissue types such as lumen, fibrous tissue, atheroma, calcification, and hemorrhage. Staining influenced segmentation results, with Van Gieson's stain excelling in fibrous tissue segmentation, while hematoxylin and eosin showed better results for calcification and hemorrhage. Moreover, the segmentation models facilitated clinical plaque classification, demonstrating good discrimination performance. Our study highlights the potential of deep neural networks in segmenting atherosclerotic plaques while emphasizing the need for careful consideration of staining effects in computerized analysis.

Atherosclerosis, a major cause of ischemic stroke worldwide, is characterized by plaque formation, particularly in the carotid bifurcation, leading to arterial stenosis. Traditional histology and light microscopy have been used to study atherosclerotic plaques, but the advent of digital pathology and artificial intelligence has provided new opportunities. In this work, we proposed an automatic segmentation method using convolutional neural networks (U-Net and DeepLabV3+) to delineate atherosclerotic carotid plaque tissue. The study included 835 images of histological slices stained with hematoxylin and eosin and Van Gieson's method from 114 patients. The results showed that DeepLabV3+ outperforms U-Net, achieving high accuracy for tissue types such as lumen, fibrous tissue, atheroma, calcification, and hemorrhage. Staining influenced segmentation results, with Van Gieson's stain excelling in fibrous tissue segmentation, while hematoxylin and eosin showed better results for calcification and hemorrhage. Moreover, the segmentation models facilitated clinical plaque classification, demonstrating good discrimination performance. Our study highlights the potential of deep neural networks in segmenting atherosclerotic plaques while emphasizing the need for careful consideration of staining effects in computerized analysis.