Multilevel Glioma Segmentation using 3D UNet Combined Attention Mechanism with Atrous Convolution Accurate segmentation of glioma from 3D medical images is vital to numerous clinical endpoints. While manual segmentation is subjective and timeconsuming, fully automated extraction is quite imperative and challenging due to the intrinsic heterogeneity of tumor structures. In this study, we propose a multilevel glioma segmentation framework, 3D ResidualAttentionAtrous UNet (RAAUNet), using 3D UNet combined attention mechanism with atrous convolution. The 3D RAAUNet can extract contextual information by combining low and highresolution feature maps. The attention mechanism is embedded in each skip connection layer of 3D RAAUNet to enhance feature representations. Meanwhile, the atrous convolution is adopted in the whole network architecture to incorporate large and rich semantic information. Furthermore, we design a new training scheme to reduce false positives and enhance generalization. Eventually, our proposed segmentation method is evaluated on the validation dataset from the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018 and achieve a competitive result with average Dice score of 88% for the whole tumor, 79% for the tumor core and 73% for the enhancing tumor, respectively. Quantitative results and visual analysis have proven that these improvements in 3D RAAUNet are effective and achieve a better segmentation accuracy compared with the baseline.

Automatic Brain Tumor Segmentation Using 3D Architecture Based on ROI Extraction MRI (magnetic resonance image) analysis is crucial for diagnosis, monitoring, and treatment of brain tumors. Manual MRI segmentation of brain tumors requires professional knowledge and costs huge amount of time. Automatic segmentation for multimodal 3D MR images is thus very desirable for clinical applications. In this paper, we present an automatic brain tumor segmentation method based on the UNet architecture, which is composed of ROI (region of interest) extraction and 3D segmentation networks with long and short skip connections. The introduced method is able to capture detailed features and improves the problem of the imbalanced classification of tumorsxe2x80x99 subregions. The method was evaluated on the BRATS 2017 dataset and the results were promising.