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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 9/2022

31-03-2022 | Original Article

Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

Authors: Shidong Li, Jianwei Liu, Zhanjie Song

Published in: International Journal of Machine Learning and Cybernetics | Issue 9/2022

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Abstract

Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.
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Metadata
Title
Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net
Authors
Shidong Li
Jianwei Liu
Zhanjie Song
Publication date
31-03-2022
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 9/2022
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-022-01536-4

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