Prostate Size Inference from Abdominal Ultrasound Images with Patch Based Prior Information

Prostate size inference from abdominal ultrasound images is crucial for many medical applications but it remains a challenging task due to very weak prostate borders and high image noise. This paper presents a novel method that enforces image patch prior information on multi-task deep learning followed by a global prostate shape estimation. The patch prior information is learned by multi-task Deep Convolutional Neural Networks (DCNNs) trained on multi-scale image patches to capture both local and global image information. We produce tens of thousands of image patches for the DCNN training that needs a large amount of training data which usually is not available for medical images. The three learned tasks for the DCNN are the distance between the patch center and the nearest contour point, the angle of the line segment between the patch center and the prostate center, and the contour curvature value for the patch center. During the prostate shape inference time, the labels returned from the multi-task DCNN are used in a global shape fitting process to obtain the final prostate contours which are then used for size inference. We performed experiments on transverse abdominal ultrasound images which are very challenging for automatic analysis.