Accurate and Robust Segmentation of the Clinical Target Volume for Prostate Brachytherapy.

We propose a method for automatic segmentation of the prostate clinical target volume for brachytherapy in transrectal ultrasound (TRUS) images. Because of the large variability in the strength of image landmarks and characteristics of artifacts in TRUS images, existing methods achieve a poor worst-case performance, especially at the prostate base and apex. We aim at devising a method that produces accurate segmentations on easy and difficult images alike. Our method is based on a novel convolutional neural network (CNN) architecture. We propose two strategies for improving the segmentation accuracy on difficult images. First, we cluster the training images using a sparse subspace clustering method based on features learned with a convolutional autoencoder. Using this clustering, we suggest an adaptive sampling strategy that drives the training process to give more attention to images that are difficult to segment. Secondly, we train multiple CNN models using subsets of the training data. The disagreement within this CNN ensemble is used to estimate the segmentation uncertainty due to a lack of reliable landmarks. We employ a statistical shape model to improve the uncertain segmentations produced by the CNN ensemble. On test images from 225 subjects, our method achieves a Hausdorff distance of 2.7 +/- 2.1 mm, Dice score of 93.9 +/- 3.5, and it significantly reduces the likelihood of committing large segmentation errors.