An Ensemble of Deep Neural Networks for Segmentation of Lung and Clavicle on Chest Radiographs

Accurate segmentation of lungs and clavicles on chest radiographs plays a pivotal role in screening, diagnosis, treatment planning, and prognosis of many chest diseases. Although a number of solutions have been proposed, both segmentation tasks remain challenging. In this paper, we propose an ensemble of deep segmentation models (enDeepSeg) that combines the U-Net and DeepLabv3+ to address this challenge. We first extract image patches to train the U-Net and DeepLabv3+ model, respectively, and then use the weighted sum of the segmentation probability maps produced by both models to determine the label of each pixel. The weight of each model is adaptively estimated according to its error rate on the validation set. We evaluated the proposed enDeepSeg model on the Japanese Society of Radiological Technology (JSRT) database and achieved an average Jaccard similarity coefficient (JSC) of 0.961 and 0.883 in the segmentation of lungs and clavicles, respectively, which are higher than those obtained by ten lung segmentation and six clavicle segmentation algorithms. Our results suggest that the enDeepSeg model is able to segment lungs and clavicles on chest radiographs with the state-of-the-art accuracy.