Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation

Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image analysis but have been performing poorly for cases when there is a significantly imbalanced feature distribution, which is true for the airway data as the trachea and principal bronchi dominate most of the voxels whereas the lobar bronchi and distal segmental bronchi occupy only a small proportion. In this paper, we propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to equalize the training progress within-class distribution. Furthermore, a Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with improved sensitivity, minimizing the variation of the distance map between the prediction and ground-truth. The proposed method is validated with the publicly available reference airway segmentation datasets.