Prostate Segmentation Using Multiparametric and Multiplanar Magnetic Resonance Images

Diseases related to the prostate and distal urethra, such as prostate cancer, benign prostatic hyperplasia and urinary incontinence, may be detected and diagnosed through noninvasive medical imaging. T2-weighted (T2W) magnetic resonance imaging (MRI) is the most commonly used modality for prostate and urethral segmentation due to its distinguishable features of anatomical texture. In addition to T2W multiplanar images, which capture information in the axial, sagittal and coronal planes, multiparametric MRI modalities such as dynamic contrast enhanced (DCE) and diffusion-weighted imaging (DWI) are usually also acquired in the scanning process to measure functional features. Feature fusion by combining multiparametric and multiplanar images is challenging due to the movement of the patient during image acquisition, the need for accurate image registration and the sheer volume of available scans. Here we propose a multi-encoder deep neural network named 3DDOSPyResidualUSENet to learn anatomical and functional features from multiparametric and multiplanar MRI images. Our extensive experiments on a public dataset show that combining T2W axial, sagittal and coronal images along with DCE information and apparent diffusion coefficient (ADC) maps computed from DWI images results in increased segmentation performance.