CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy

Accurate segmentation of organs-at-risk is important in prostate cancer radiation therapy planning. However, poor soft tissue contrast in CT makes the segmentation task very challenging. We propose a deep convolutional neural network approach to automatically segment the prostate, bladder, and rectum from pelvic CT. A hierarchical coarse-to-fine segmentation strategy is used where the first step generates a coarse segmentation from which an organ-specific region of interest (ROI) localization map is produced. The second step produces detailed and accurate segmentation of the organs. The ROI localization map is generated using a 3D U-net. The localization map helps adjusting the ROI of each organ that needs to be segmented and hence improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we designed a fully convolutional network (FCN) by combining a generative adversarial network (GAN) with a U-net. Specifically, the generator is a 3D U-net that is trained to predict individual pelvic structures, and the discriminator is an FCN which fine-tunes the generator predicted segmentation map by comparing it with the ground truth. The network was trained using 100 CT datasets and tested on 15 datasets to segment the prostate, bladder and rectum. The average Dice similarity (mean±SD) of the prostate, bladder and rectum are 0.90±0.05, 0.96±0.06 and 0.91±0.09, respectively, and Hausdorff distances of these three structures are 5.21±1.17, 4.37±0.56 and 6.11±1.47 (mm), respectively. The proposed method produces accurate and reproducible segmentation of pelvic structures, which can be potentially valuable for prostate cancer radiotherapy treatment planning.