Deep Support Vein Machine for Lung Parcellation

Abstract

Pulmonary segments parcellation is essential to thoracoscopic segmentectomy. Surgeons manually outline pulmonary segments from preoperative images before surgery, which is a time-consuming, labor-intensive and mental-stress procedure. This work proposes a novel small learning model of deep support vein machine without using annotated pulmonary segments data for automatic lung parcellation. Specifically, this machine can learn anatomical structures of pulmonary lobe, bronchus, artery, and vein by two cascade multilayer perceptrons to automatically divides the lung into eighteen segments. The perceptron module typically smooths the boundary of the pulmonary segment to attain robust and precise parcellation. Additionally, three new metrics are defined to quantitatively evaluate the quality of lung parcellation. We validate our methods on 108 clinical pulmonary computed tomography scans, with the experimental results showing that our proposed machine certainly outperforms current methods and provides a promising way to fully automated lung parcellation. Particularly, the dice similarity coefficient of lung parcellation was significantly improved from 0.886 to 0.918.