Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation

Abstract

The brain tumor segmentation task aims to classify sub-regions into peritumoral edema, necrotic core, enhancing and non-enhancing tumor core using multimodal MRI scans. This task is very challenging due to its intrinsic high heterogeneity of appearance and shape. Recently, with the development of deep models and computing resources, deep convolutional neural networks have shown their effectiveness on brain tumor segmentation from 3D MRI cans, obtaining the top performance in the MICCAI BraTS challenge 2017. In this paper we further boost the performance of brain tumor segmentation by proposing a multi-scale masked 3D U-Net which captures multi-scale information by stacking multi-scale images as inputs and incorporating a 3-D Atrous Spatial Pyramid Pooling (ASPP) layer. To filter noisy results for tumor core (TC) and enhancing tumor (ET), we train the TC and ET segmentation networks from the bounding box for whole tumor (WT) and TC, respectively. On the BraTS 2018 validation set, our method achieved average Dice scores of 0.8094, 0.9034, 0.8319 for ET, WT and TC, respectively. On the BraTS 2018 test set, our method achieved 0.7690, 0.8711, and 0.7792 dice scores for ET, WT and TC, respectively. Especially, our multi-scale masked 3D network achieved very promising results enhancing tumor (ET), which is hardest to segment due to small scales and irregular shapes.