A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net

Abstract

Accurate segmentation of different brain tumor regions from MR images is of great significance in the diagnosis and treatment of brain tumors. In this paper, an enhanced 3D U-Net model was proposed to address the shortcomings of 2D U-Net in the segmentation tasks of brain tumors. While retaining the U-shaped characteristics of the original U-Net network, an enhanced encoding module and decoding module were designed to increase the extraction and utilization of image features. Then, a hybrid loss function combining the binary cross-entropy loss function and dice similarity coefficient was adopted to speed up the model’s convergence and to achieve accurate and fast automatic segmentation. The model’s performance in the segmentation of brain tumor’s whole tumor region, tumor core region, and enhanced tumor region was studied. The results showed that the proposed 3D U-Net model can achieve better segmentation performance, especially for the tumor core region and enhanced tumor region tumor regions.