Vessel lumen segmentation in internal carotid artery ultrasounds with deep convolutional neural networks

Carotid ultrasound is a screening modality used by physicians to direct treatment in the prevention of ischemic stroke in high-risk patients. It is a time intensive process that requires highly trained technicians and physicians. Evaluation of a carotid ultrasound requires identification of the vessel wall, lumen, and plaque of the carotid artery. Automated machine learning methods for these tasks are highly limited. We propose and evaluate here single and multi-path convolutional U-neural network for lumen identification from ultrasound images. We obtained de-identified images under IRB approval from 98 patients. We isolated just the internal carotid artery ultrasound images for these patients giving us a total of 302 images. We manually segmented the vessel lumen, which we use as ground truth to develop and validate our model. With a basic simple convolutional U-Net we obtained a 10-fold cross-validation accuracy of 95%. We also evaluated a dual-path U-Net where we modified the original image and used it as a synthetic modality but we found no improvement in accuracy. We found that the sample size made a considerable difference and thus expect our accuracy to rise as we add more training samples to the model. Our work here represents a first successful step towards the automated identification of the vessel lumen in carotid artery ultrasound images and is an important first step in creating a system that can independently evaluate carotid ultrasounds.