Automated Segmentation of Breast Arterial Calcifications from Digital Mammography Breast arterial calcifications (BACs) are formed when calcium is deposited in the walls of arteries in the breast. The accurate segmentation of BACs is a critical step for risk assessment of cardiovascular disease from a mammogram. This paper evaluates the performance of three deep learning architectures, YOLO, Unet and DeepLabv3, on detecting BACs in digital mammography. In comparison, a simple Hessianbased multiscale filter is developed to enhance BACs pattern, then a selfadaptive thresholding algorithm is applied to obtain the binary mask of BACs. As BACs are relatively small in size, we developed a new metric to better evaluate the small object segmentation. In this study, 135 digital mammographic images containing labelled BACs were obtained, in which 80% for training deep learning networks and 20% for validation. The results show that our Hessianbased filtering method achieves a highest accuracy on validation data, and DeepLabv3 falls behind with little effectiveness. We conclude simple filtering technique is effective in BACs extraction, and DeepLabv3 is an expensive alternative in terms of its computational cost and configuration complexity.

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.