2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation

摘要

Brain tumour segmentation is a requirement of many quantitative MRI analyses involving glioma. This paper argues that 2D slice-wise approaches to brain tumour segmentation may be more compatible with current MRI acquisition protocols than 3D methods because clinical MRI is most commonly a slice-based modality. A 2D Dense-UNet segmentation model was trained on the BraTS 2020 dataset. Mean Dice values achieved on the test dataset were: 0.859 (WT), 0.788 (TC) and 0.766 (ET). Median test data Dice values were: 0.902 (WT), 0.887 (TC) and 0.823 (ET). Results were comparable to previous high performing BraTS entries. 2D segmentation may have advantages over 3D methods in clinical MRI datasets where volumetric sequences are not universally available.