Two-Stage Deep Learning with Multi-Pathway Network for Brain Tumor Segmentation and Malignancy Identification From MR Images

Accurately segmenting contrast-enhancing brain tumors plays an important role in surgical planning of high-grade gliomas. Also, precisely stratifying malignancy risk within non-enhancing T2 hyperintense area helps control the radiation dose according to the malignancy risk and prevent normal brain tissue from being unnecessarily exposed to radiation. In this work, we 1) segment brain tumors using deep learning, and 2) provide more detailed segmentation results that can show the malignancy risk within the T2 high region. We utilize a two-stage framework where we make images with restricted ROI through foreground cropping so that the model can focus on only tumor part.