Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging.

Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging. Phys Med Biol. 2020 Jul 16;: Authors: Jeong J, Lei Y, Kahn S, Liu T, Curran WJ, Shu HK, Mao H, Yang X Abstract The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. They play a crucial role in providing pre-operative assessment of tumor histology, grading, and tumor biopsy guidance. However, the manual contouring of these neoplasms is tedious, expensive, time-consuming, and contains inter-observer variability. In this work, we propose using a 3D Mask R-CNN method to automatically segment brain tumors for DSCE MRI perfusion images. We used a 3D Mask region-based convolutional neural network (R-CNN) to segment the DSCE perfusion tumor. As our goal was to simultaneously localize and segment the tumor, our training process contained both a region-of-interest (ROI) localization and regression with voxel-wise segmentation. The combination of classification loss, ROI location and size regression loss, and segmentation loss were used to supervise the proposed network. We retrospectively investigated 21 patients' perfusion images, with b...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research