A deep multi-task learning framework for brain tumor segmentation
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Published version
Author(s)
Type
Journal Article
Abstract
Glioma is the most common primary central nervous system tumor, accounting for about half
of all intracranial primary tumors. As a non-invasive examination method, MRI has an
extremely important guiding role in the clinical intervention of tumors. However, manually
segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects
the implementation of follow-up diagnosis and treatment plans. With the development of deep
learning, medical image segmentation is gradually automated. However, brain tumors are
easily confused with strokes and serious imbalances between classes make brain tumor
segmentation one of the most difficult tasks in MRI segmentation. In order to solve these
problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion
module in the framework to accurately segment brain tumors. In this framework, we have
added a distance transform decoder based on the V-Net, which can make the segmentation
contour generated by the mask decoder more accurate and reduce the generation of rough
boundaries. In order to combine the different tasks of the two decoders, we weighted and added
their corresponding loss functions, where the distance map prediction regularized the mask
prediction. At the same time, the multi-depth fusion module in the encoder can enhance the
ability of the network to extract features. The accuracy of the model will be evaluated online
using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets.
This method obtains high-quality segmentation results, and the average Dice is as high as 78%.
The experimental results show that this model has great potential in segmenting brain tumors
automatically and accurately.
of all intracranial primary tumors. As a non-invasive examination method, MRI has an
extremely important guiding role in the clinical intervention of tumors. However, manually
segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects
the implementation of follow-up diagnosis and treatment plans. With the development of deep
learning, medical image segmentation is gradually automated. However, brain tumors are
easily confused with strokes and serious imbalances between classes make brain tumor
segmentation one of the most difficult tasks in MRI segmentation. In order to solve these
problems, we propose a deep multi-task learning framework and integrate a multi-depth fusion
module in the framework to accurately segment brain tumors. In this framework, we have
added a distance transform decoder based on the V-Net, which can make the segmentation
contour generated by the mask decoder more accurate and reduce the generation of rough
boundaries. In order to combine the different tasks of the two decoders, we weighted and added
their corresponding loss functions, where the distance map prediction regularized the mask
prediction. At the same time, the multi-depth fusion module in the encoder can enhance the
ability of the network to extract features. The accuracy of the model will be evaluated online
using the multispectral MRI records of the BraTS 2018, BraTS 2019, and BraTS 2020 datasets.
This method obtains high-quality segmentation results, and the average Dice is as high as 78%.
The experimental results show that this model has great potential in segmenting brain tumors
automatically and accurately.
Date Issued
2021-06-04
Date Acceptance
2021-05-17
Citation
Frontiers in Oncology, 2021, 11
ISSN
2234-943X
Publisher
Frontiers Media
Journal / Book Title
Frontiers in Oncology
Volume
11
Copyright Statement
© 2021 Huang, Yang, Zhang, Xu, Yang, Jiang and Lai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Subjects
automatic segmentation
brain tumor
deep multi-task learning framework
magnetic resonance imaging
multi-depth fusion module
1112 Oncology and Carcinogenesis
Publication Status
Published
Article Number
ARTN 690244