Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion
  Prior   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.

16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom