3D PBV-Net: An automated prostate MRI data segmentation method. (January 2021)

• Record Type: Journal Article
• Authors: Jin, Yao; Yang, Guang; Fang, Ying; Li, Ruipeng; Xu, Xiaomei; Liu, Yongkai; Lai, Xiaobo
• Is Part Of: Computers in biology and medicine. Volume 128(2021)
• Publication Date: 2021-01
• Subjects: Prostate cancer -- MRI -- Automated segmentation -- Telehealth care -- Enabling technology; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.104160
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Prostate cancer is one of the most common deadly diseases in men worldwide, which is seriously affecting people's life and health. Reliable and automated segmentation of the prostate gland in MRI data is exceptionally critical for diagnosis and treatment planning of prostate cancer. Although many automated segmentation methods have emerged, including deep learning based approaches, segmentation performance is still poor due to the large variability of image appearance, anisotropic spatial resolution, and imaging interference. This study proposes an automated prostate MRI data segmentation approach using bicubic interpolation with improved 3D V-Net (dubbed 3D PBV-Net). Considering the low-frequency components in the prostate gland, the bicubic interpolation is applied to preprocess the MRI data. On this basis, a 3D PBV-Net is developed to perform prostate MRI data segmentation. To illustrate the effectiveness of our approach, we evaluate the proposed 3D PBV-Net on two clinical prostate MRI data datasets, i.e., PROMISE 12 and TPHOH, with the manual delineations available as the ground truth. Our approach generates promising segmentation results, which have achieved 97.65% and 98.29% of average accuracy, 0.9613 and 0.9765 of Dice metric, 3.120 mm and 0.9382 mm of Hausdorff distance, and average boundary distance of 1.708, 0.7950 on PROMISE 12 and TPHOH datasets, respectively. Our method has effectively improved the accuracy of automated segmentation of the prostate MRI data and is promising to meet the accuracy requirements for telehealth applications. Highlights: A novel deep model is proposed for automated prostate MRI data segmentation. Obtained average accuracy of 97.65% and 98.29% for prostate MRI data segmentation. Multiple evaluation metrics have been used to validate our proposed method. The proposed method is robust and can achieve promising results on two datasets.