Clinical target-volume delineation in prostate brachytherapy using residual neural networks

Abstract

Low dose-rate prostate brachytherapy is commonly used to treat early stage prostate cancer. This intervention involves implanting radioactive seeds inside a volume containing the prostate. Planning the intervention requires obtaining a series of ultrasound images from the prostate. This is followed by delineation of a clinical target volume, which mostly traces the prostate boundary in the ultrasound data, but can be modified based on institution-specific clinical guidelines. Here, we aim to automate the delineation of clinical target volume by using a new deep learning network based on residual neural nets and dilated convolution at deeper layers. In addition, we propose to include an exponential weight map in the optimization to improve local prediction. We train the network on 4,284 expert-labeled transrectal ultrasound images and test it on an independent set of 1,081 ultrasound images. With respect to the gold-standard delineation, we achieve a mean Dice similarity coefficient of 94%, a mean surface distance error of 1.05 mm and a mean Hausdorff distance error of 3.0 mm. The obtained results are statistically significantly better than two previous state-of-the-art techniques.