An Enhanced UNet for Brain Tumor Segmentation The Convolutional Neural Network (CNN) has been in rapid development these years. It is applied to many fields. Among these, brain tumor segmentation is one of foci. Researchers delves into this with different ways. Some researchers use 3D CNN to handle the brain images for tumor segmentation, and others use 2D CNN modified from models of natural images in semantic segmentation to process separate slices of brain images. Based on properties of brain images and the inherent contradiction of semantic segmentation, we chose 2D UNet as basic model and modified the skip connection in it to propose our model. To verify effect of our model, we introduced our brain image dataset and made experiments on it and BraTS 2017 dataset. Experiments shows that proposed model outperforms the original UNet.

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.