Tailored Methods for Segmentation of Intravascular Ultrasound Images via Convolutional Neural Networks

Automatic delineation of relevant structures in intravascular imaging can support percutaneous coronary interventions (PCIs), especially when dealing with rather demanding cases. State-of-the-art segmentation performances on intravascular ultrasound (IVUS) images are usually achieved with convolutional neural networks (CNNs). We found three major error types which occur regularly when segmenting lumen and wall of morphologically complex vessels with CNNs. In order to reduce these three error types, we developed three IVUS-specific methods which are able to improve generalizability of state-of-the-art CNNs for IVUS segmentation tasks. These methods are based on three concepts: speckle statistics, artery shape priors via independent component analysis (ICA) and the concentricity condition of lumen and vessel wall. We found that all three methods outperform the baseline. Maximum improvements are 2.3 % by means of the Dice coefficient and 59.2 % by means of the modified Hausdorff distance. Since all three concepts can be readily transferred to intravascular optical coherence tomography (IVOCT), we expect these findings can support the segmentation of corresponding images as well.