Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging
Jiwoong Jeong, Yang Lei, Hui‐Kuo G. Shu, Tian Liu, Liya Wang, Walter J. Curran, Hui Mao, Xiaofeng Yang
Abstract
The detection and segmentation of neoplasms are 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 (DSC) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. However, the manual contouring of these neoplasms are tedious, expensive, time-consuming, and contains inter-observer variability. In this work, we propose to use a 3D Mask R-CNN method to automatically detect and segment high and low grade brain tumors for DSC MRI perfusion images. Twenty-two high and low grade patients with 50-70 perfusion time-point volumes were used in this study. Experimental results show that our proposed method achieved an overall Dice similarity, precision, recall and center of mass distance were 89%±0.03%, 90%±0.02%, 87%±0.04% and 1.27±0.67 respectively.