Lumbar Vertebrae Synthetic Segmentation in Computed Tomography Images Using Hybrid Deep Generative Adversarial Networks

Abstract

The lumbar vertebrae segmentation in Computed tomography (CT) is challenging due to the scarcity of the labeled training data that we define as paired training data for the deep learning technique. Much of the available data is limited to the raw CT scans, unlabeled by radiologists. To handle the scarcity of labeled data, we utilized a hybrid training system by combining paired and unpaired training data and construct a hybrid deep segmentation generative adversarial network (Hybrid-SegGAN). We develop a total automatic approach for lumbar vertebrae segmentation in CT images using Hybrid-SegGAN for synthetic segmentation. Our network receives paired and unpaired data, discriminates between the two sets of data, and processes each through separate phases. We used CT images from 120 patients to demonstrate the performance of the proposed method and extensively evaluate the segmentation results against their ground truth by using 12 performance measures. The result analysis of the proposed method suggests its feasibility to improve the capabilities of deep learning segmentation without demanding the time-consuming annotation procedure for labeled and paired data.