VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation

Abstract

Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data. We propose an automatic and robust vessel segmentation approach that uses a multi-pathways deep learning network. The proposed method trains a deep network for binary classification based on extracted training patches on three planes (sagittal, coronal, and transverse planes) centered on the focused voxels. Thus, it is expected to provide a more reliable recognition performance by exploring the 3D structure. Furthermore, due to the large variety of medical data device values, we transform a raw medical image into a probability map as input to the network. Then, we extract vessels based on the proposed network, which is robust and sufficiently general to handle images with different contrast obtained by various imaging systems. The proposed deep network provides a vessel probability map for voxels in the target medical data, which is used in a post-process to generate the final segmentation result. To validate the effectiveness and efficiency of the proposed method, we conducted experiments with 20 data (public datasets) with different contrast levels and different device value ranges. The results demonstrate impressive performance in comparison with the state-of-the-art methods. We propose the first 3D liver vessel segmentation network using deep learning. Using a multi-pathways network, segmentation results can be improved, and the probability map as input is robust against intensity changes in clinical data.