Simple MyUnet3D for BraTS Segmentation

摘要

The deep learning architectures that have been used for brain tumor segmentation in the BraTS challenge have performed well for the WT, TC, and ET segmentations. However, these architectures generally have many parameters and require large storage capacity for the model. In this paper, we propose a Simple MyUnet3D to do segmentation on BraTS 2018 dataset. This proposed architecture was inspired by 2D U-Net and modified to do 3D image segmentation. Dataset divides into 2 parts, one part of training and the other for validation. From 285 data, 213 for training, and 72 for validating the model. The segmentation consists of 3 parts, whole tumor(WT), tumor core(TC), and enhanced tumor(ET). Even its simplicity, it produces a dice coefficient of 0.8269 at segmenting the whole tumor. Nevertheless, its performance in tumor core and enhanced tumor need to be developed. The simplicity and its result in segmenting the whole tumor have great potential to be better developed.