Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images

Automated segmentation of organs-at-risk (OARs) in pelvic computed tomography (CT) images can assist radiotherapy treatment planning by saving efforts for manual contouring and reducing intra-observer and inter-observer variations. However, training effective deep-learning segmentation models usually require a sufficient amount of high-quality labeled data, which are costly to collect. Taking automated segmentation of OARs as a case study, we developed a novel semi-supervised adversarial deep learning approach to support medical image data processing and modeling where training data may be insufficient. Unlike supervised deep learning methods, our new approach can utilize both annotated and un-annotated data for training. Additionally, the new approach can generate un-annotated data by the generative adversarial networks (GANs) aided data augmentation scheme. We applied the new approach to segmenting tumors and multiple OARs in male pelvic CT images. The new approach was evaluated on a dataset of 100 training cases and 20 testing cases. Experimental results, including four metrics (dice similarity coefficient, average Hausdorff distance, average surface Hausdorff distance, and relative volume difference), showed that the new method can achieve comparable performance with less annotated data and better performance with the same amount of annotated data. The performance of the new approach and its 3D Pelvic CT segmentation model achieved comparable or better performance than other state-of-the-art methods.