Automated Stenosis Detection and Classification in Xray Angiography Using Deep Neural Network This paper proposes a deeplearning based workflow for stenosis classification and localization on coronary angiography images of 194 patients from a multicenter study. Coronary stenosis severities were categorized into three groups of  25% stenosis as 2CAT for classification training labels; and stenosis bounding boxes were annotated in images as stenosis localization labels, based on expert physicianu0027s visual reading on right coronary artery (RCA) and left coronary artery (LCA) images with full contrast filling of the coronary artery. CNN and recurrent neural network models were employed for coronary artery view classification, candidate frame selection and stenosis classification. Furthermore, stenosis activation maps were implemented for weaklysupervised stenoses positioning. In experiments, our method achieved 0.910.85 AUC values for 3CAT stenosis classification in RCA and LCA respectively; and 0.910.87 AUC values for 2CAT classification in RCA and LCA respectively. For stenosis detection on most significant regions, sensitivity for RCALCA were 0.720.60 respectively; and mean square error between detection and groundtruth center points were 69.679.5 pixels for RCALCA in image with size of  512ast512 . The results show our method achieves high performance in stenosis severity classification and performs reasonably well for stenosis positioning.

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.