Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution
Bo Wang, Yang Lei, Jason J. Jeong, Tonghe Wang, Yingzi Liu, Sibo Tian, Pretesh R Patel, Xiaojun Jiang, Ashesh B. Jani, Hui Mao, Walter John Curran, Tian Liu, Xiaofeng Yang · Medical Imaging 2019: Computer-Aided Diagnosis · 2019
Prostate segmentation of MR volumes is a very important task for treatment planning and image-guided brachytherapy and radiotherapy. Manual delineation of prostate in MR image is very time-consuming and depends on the subjective experience of the physicians. On the other hand, automatic prostate segmentation becomes a reasonable and attractive choice for its speed, even though the task is very challenging because of inhomogeneous intensity and variability of prostate appearance and shape. In this paper, we propose a method to automatically segment MR prostate image based on 3D deeply supervised FCN with concatenated atrous convolution (3D DSA-FCN). More discriminative features provide explicit convergence acceleration in training stage using straightforward dense predictions as deep supervision and the concatenated atrous convolution extract more global contextual information for accurate predictions. The presented method was evaluated on the internal dataset comprising 15 T2-weighted prostate MR volumes from Winship Cancer Institute and obtained a mean Dice similarity coefficient (DSC) of 0.852±0.031, 95% Hausdorff distance (95%HD) 7.189±1.953 mm and mean surface distance (MSD) of 1.597±0.360 mm. The experimental results show that our 3D DSA-FCN could yield satisfied MR prostate segmentation, which can be used for image-guided radiotherapy.