Abstract P379: Automatic Segmentation of Atherosclerotic Plaques in Transverse Carotid Ultrasound Images Using Deep Learning

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Purpose: Ultrasound imaging is commonly used for patients with atheroscelerotic plaques in the carotid artery. While B-mode ultrasound can be used for detection and measurement of these plaques, interpreting these images can be a subjective and time-consuming task. Deep learning algorithms have been proven to be an effective tool for interpreting medical images, especially for classification and segmentation tasks. Here, we propose a deep learning model to automatically detect and measure plaques in transverse B-mode images of the carotid artery. Methods: The proposed automated method takes a transverse B-mode image of the carotid artery as an input and segments the vessel wall in the transverse cross section image using convolutional neural networks. To ensure that the method can perform well in clinical settings, the method has been evaluated on not only healthy subjects (max IMT below 1.2 mm) but also on patients with atheroscelerotic plaques and other vessel wall pathology. Given the B-mode transverse image as an input, the neural network first finds a region of interest (ROI) surrounding the artery and then segments both the inner and outer wall of the carotid artery. We determined the accuracy of the system by F1 Score, a common metric to evaluate the performance of machine learning algorithms. Results: The network was trained and tested on a transverse ultrasound carotid artery dataset that has 506 images, gathered from 4 hospitals. Annotations of an expert reader were used as the ground truth and the performance of the method was evaluated using 5-fold cross validation. The proposed method reaches an F1 score of 0.91 for correctly detecting the ROI and an F1 score of 0.78 for detecting and segmenting the vessel walls in transverse B-mode images. Conclusions: The results show that the proposed deep learning method can be used for accurate analysis and interpretation of carotid ultrasound scans in a clinical setting and potentially reduce the reporting time while increasing objectivity of the analysis.