Automated Prostate Segmentation of Volumetric CT images Using 3D Deeply Supervised Dilated FCN

Segmentation of the prostate in 3D CT images is a crucial step in treatment planning and procedure guidance such as brachytherapy and radiotherapy. However, manual segmentation of the prostate is very time-consuming and depends on the experience of the clinician. On the contrary, automated prostate segmentation is more helpful in practice, whereas the task is very challenging due to low soft-tissue contrast in CT images. In this paper, we propose a 3D deeply supervised fully-convolutional-network (FCN) with dilated convolution kernel to automatically segment prostate in CT images. A deep supervision strategy could acquire more powerful discriminative capability and accelerate the optimization convergence in training stage, while concatenating the dilated convolution enlarges the receptive field to extract more global contextual information for accurate prostate segmentation. The presented method was evaluated using 15 prostate CT images and obtained a mean Dice similarity coefficient (DSC) of 0.85 +/- 0.04 and mean surface distance (MSD) of 1.92 +/- 0.46 mm. The experimental results show that our approach yields accurate CT prostate segmentation, which can be employed for the prostate-cancer treatment planning of brachytherapy and external beam radiotherapy.