Neural Network Boundary Detection for 3D Vessel Segmentation

Conventionally, hand-crafted features are used to train machine learning algorithms, however choosing useful features is not a trivial task as they are very much data-dependent. Given raw image intensities as inputs, supervised neural networks (NNs) essentially learn useful features by adjusting the weights of its nodes using the back-propagation algorithm. In this paper we investigate the performance of NN architectures for the purpose of boundary detection, before integrating a chosen architecture in a data-driven deformable modelling framework for full segmentation. Boundary detection performed well, with boundary sensitivity of > 88% and specificity of > 85% for highly obscured and diffused lymphatic vessel walls. In addition, the vast majority of all boundary-classified pixels were in the immediate vicinity of the ground truth boundary. When integrated into a 3D deformable modelling framework it produced an area overlap with the ground truth of > 98%, and both point-to-mesh and Hausdorff distance errors were less than other approaches. To this end it has been shown that NNs are suitable for boundary detection in deformable modelling, where object boundaries are obscured, diffused and low in contrast.