SC-Former: A Segmentation Convolution Transformer for Lung Surgery Robots

Abstract

For lung surgery robots, the precise segmentation of pulmonary fissures is very important. Damaging the inter-lobar fissures during surgery can have serious consequences. Accurately segmenting weak and abnormal fissures commonly found in clinical CT scans remains a challenging task. To solve the above problem, we aimed to develop a novel Convolution Transformer for accurate fissure segmentation (SC-Former). The proposed SC-Former adopts an encoder, attention block, and decoder structure. First, we designed an encoder with a hybrid CNNs-transformer block that ingeniously amalgamates coordinate convolution and coordinate transformer to effectively capture both local and global feature information. Second, we introduced the long skip connections of our designed attention block at layers of the decoder-encoder structure to emphasize the field of view for fissures. Third, we added the distance map strategy to alleviate the challenge of training the network to segment the false positives from the complex textures in the lung. Fourth, we developed a multi-scale supervision strategy for independent prediction at various decoder levels, effectively integrating multi-scale semantic information to facilitate the segmentation of weak and abnormal fissures. Because of the lack of open-source inter-pulmonary fissure datasets, we collected 3D CT scans from 400 participants in the clinical trial and created a new high-quality dataset: BMI dataset. Extensive experiments on this dataset revealed the great superiority of our method over several state-of-the-art competitors. The ablation study also validated the effectiveness and robustness of each part of SC-Former.