Atherosclerotic Vascular Calcification Detection and Segmentation on Low Dose Computed Tomography Scans Using Convolutional Neural Networks

We propose an automated platform for extra-coronary calcification detection on low dose CT scans. We utilize faster regional convolutional neural networks (R-CNN) to directly detect calcifications at the lesion-level without performing vessel extraction. To segment detected calcifications at the voxel-level, we employ holistically nested edge detection (HED). CT scans of 112 vasculitis patients and 3219 images with labeled calcifications were used to develop and evaluate our method. By employing a two-class faster R-CNN, the average precision (AP) increased from 49.2% to 84.4% for calcification detection. In addition, sensitivity of 85.0% at 1 false positive per image was observed. The Dice Similarity Coefficient (DSC) for calcification segmentation using HED (0.83 +/- 0.08) was significantly better (p<< 0.01) than the traditional threshold-based method (0.59 > 0.26).