Deep Learning for Prostate Segmentation
Segmentation of the prostate in computed tomography (CT) is used for planning and guidance of prostate treatment procedures. However, due to the low soft-tissue contrast of the images, manual delineation of the prostate on CT is a time-consuming task with high inter-observer variability. In this study, we developed an automatic, three-dimensional (3D) prostate segmentation algorithm based on a customized U-Net architecture. An automatic 3D deep learning-based segmentation algorithm was designed to address the need for a fast, accurate, and repeatable 3D segmentation of the prostate on CT images, which does not depend on the intra-patient data for training. The proposed neural network has not been trained on previously acquired images from the target patient. We evaluated the performance of the algorithm to the manual references from two expert radiologists, and we used the complementary region-based (DSC, SR, and PR), surface-based (MAD), and volume based (ΔV) error metrics to measure the segmentation error of our algorithm against manual references. We also compared the results to the measured inter-expert observer difference in manual segmentation. The proposed algorithm showed robustness to some of the image artifacts caused by metallic implanted objects.