Automatic Prostate Segmentation on 3D MRI Scans Using Convolutional Neural Networks with Residual Connections and Superpixels

Abstract

Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Notwithstanding, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients. Therefore, this paper proposes an automatic method for prostate segmentation on 3D MRI scans using a content-sensitive superpixels technique, deep convolutional neural networks with residual connections (ResCNN), and the particle swarm optimization (PSO) algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a Dice similarity coefficient of 86.68 %, Jaccard index of 76.58%, relative volume difference of 3.92%, volumetric similarity of 96.69 %, sensitivity of 88.36 %, specificity of 93.43%, accuracy of 91.97% and an area under ROC curve of 90.90 %. Experimental results demonstrate the high performance-potential of the proposed method comparable to those previously published.