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

Jiwoong Jeong; Yang Lei; Hui-Kuo Shu; Tian Liu; Liya Wang; Walter Curran; Hui Mao; Xiaofeng Yang

doi:10.1117/12.2549288

28 February 2020 Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

Jiwoong Jeong, Yang Lei, Hui-Kuo Shu, Tian Liu, Liya Wang, Walter Curran, Hui Mao, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1131720 (2020) https://doi.org/10.1117/12.2549288
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Jiwoong Jeong, Yang Lei, Hui-Kuo Shu, Tian Liu, Liya Wang, Walter Curran, Hui Mao, and Xiaofeng Yang "Brain tumor segmentation using 3D mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131720 (28 February 2020); https://doi.org/10.1117/12.2549288

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