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

Physics in Medicine and Biology
Jiwoong JeongXiaofeng Yang

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 between 50-...Continue Reading

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Feb 28, 2019·Medical Physics·Xue DongXiaofeng Yang

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Citations

Mar 4, 2021·Medical Physics·Yang LeiXiaofeng Yang
May 17, 2021·Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)·Yabo FuXiaofeng Yang

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