Brain tumor segmentation is highly important for clinical management. We propose HUMBLe, a hierarchical 3D U-Net for MRI Brain Lesion segmentation architecture. HUMBLe breaks down the segmentation into its separate classes: enhancing tumor, edema, and necrotic classes, and uses a classifier to merge the different segmentation results into a final segmentation mask. Evaluation was performed on multi-parametric longitudinal local dataset, of patients with Glioblastoma. Segmentation results obtained by HUMBLe on our cohort improved DICE scores by 7%-16% for the different tumor components, compared to segmentation performed using 3D U-Net based architecture trained on BraTS2019 and our cohort.
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