Lung Tumor Segmentation using Coupling-Net with Shape-focused Prior on Chest CT Images of Non-small Cell Lung Cancer Patients

摘要

Volumetric lung tumor segmentation is essential for monitoring tumor response to treatment by tracking lung tumor changes. However, it is difficult to segment due to the diversity of size, shape, location as well as types such as solid, sub-solid and necrosis of lung tumor and it is difficult to distinguish between the tumor and the nearby structures because of their low contrast in case of tumors attached to chest wall or mediastinum. In this study, we propose a coupling-net with shape-focused prior that focuses on segmentation of various types of lung tumor and prevent leakage into nearby structures. First, to extract shape information. 2D-Net is trained in each axial, coronal, and sagittal planes. Second, to generate the shape-focused prior including suspicious area of the lung tumor, the prediction maps are integrated with maximum voting, and shape-focused prior was made by applying the narrow-band distance propagation. Finally, to prevent leakage due to low contrast between lung tumor and adjacent structures and give the constraint using shape-focused prior, a 3D-Net is trained using shape-focused prior. To validate segmentation performance, we divide into four types according to the tumor location characteristics: non-attached tumor (Type 1), chest wall-attached tumor (Type 2), mediastinum-attached tumor (Type 3), and surrounded-tumor by chest wall, or liver in apex and base in the lung (Type 4). Our proposed network showed best segmentation performance without a leak to adjacent structures due to considering shape-focused prior.