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 highrisk 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 multipath convolutional Uneural network for lumen identification from ultrasound images. We obtained deidentified 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 UNet we obtained a 10fold crossvalidation accuracy of 95%. We also evaluated a dualpath UNet 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.

Neural Network Training Data Profoundly Impacts TextureBased Intravascular Image Segmentation Segmentation of variably differentiated and lowfrequency elements in a complex image is challenging. Improving sensitivity demands often prohibitive decreases in specificity. This is particularly the case in intravascular imaging, where detection of heterogeneously dispersed lesion elements, which are often less evident than normal structures, is essential. Modalities including optical coherence tomography (OCT) provide crosssectional images of coronary arteries that reveal atherosclerotic plaques. Manual plaque segmentation is time consuming and error prone; automated methods are quicker but dictate accuracy tradeoffs. We developed a neural networkbased method for automatic detection of calcified plaques in OCT images using texturebased features and examined how underlying training data distribution impacts sensitivity and predictive value. The method assesses each pixel, rather than a patch, as an independent unit, enabling precise control of training data distribution while simultaneously decreasing reliance on massive imaging datasets for training. Pixels from 30 manually annotated OCT images of calcified plaques were used to train the neural network. Several texture measures were computed for the local neighborhood of each pixel and used as inputs to a multilayered neural network. The ratio of pixels of each class in the training dataset was then varied and the resulting network performance was compared. Positive predictive value and sensitivity ranged from 0.69 to 0.77 and 0.35 to 0.86, respectively, as the ratio of noncalcified to calcified pixels varied from around 15 to 1, with inverse changes in specificity. The results clearly demonstrate that appropriately balanced data must be carefully curated with thoughtful consideration of the modelu0027s application and the clinical imperative being addressed.