We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. First, we utilize the ground-truth brain tumor lesions from a group of patients to generate the heatmaps of different types of lesions. These heatmaps are used to create the volume-of-interest (VOI) map which contains prior information about brain tumor lesions. The VOI map is then integrated with the multimodal MR images and input to a 3D U-Net for segmentation. The proposed method is evaluated on a public benchmark dataset, and the experimental results show that the proposed feature fusion method achieves an improvement over the baseline methods. In addition, our proposed method also achieves a competitive performance compared to state-of-the-art methods.
@article{arxiv.1907.00281,
title = {Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior},
author = {Po-Yu Kao and Jefferson W. Chen and B. S. Manjunath},
journal= {arXiv preprint arXiv:1907.00281},
year = {2020}
}