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Automated brain tumor segmentation using cascaded bootstrapping model
Author(s): Lihao Qin; Haifeng Zhao; Shaojie Zhang; Zhenyu Tang
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Paper Abstract

Brain tumor segmentation is an essential task in image based brain tumor analysis. Many deep learning based (DL) brain tumor segmentation methods have been proposed. They usually segment the brain tumor into three different sub-regions, i.e., the edema (ED), the enhancing tumor (ET) and the necrotic (NCR) from input multi-modal brain images. Some of them treat the brain tumor segmentation as a multi-label classification task, where voxels in the input brain images are classified into one of the four classes (i.e., 3 sub-regions + 1 background) simultaneously. Currently proposed methods decompose the multi-label classification task into separate binary classification tasks, each of which is responsible for classifying one of the three sub-regions. Since binary classification is easier to be optimized than multi-label classification, more accurate segmentation can be achieved by binary classification. However, most of binary classification based methods are not end-to-end, i.e., each DL model is trained to segment one tumor sub-regions separately, and correlations among them are ignored. To solve this issue, in this paper, we propose an end-to-end cascaded bootstrapping DL model, which considers the correlations among different binary classification tasks. Our model contains three levels, through which the whole tumor (WT), the tumor core (TC) and ET are segmented in a cascaded way. More important, each level estimates the corresponding segmentation using the features learnt from the previous level. Moreover, a multi-view fusion is adapted to further boost the segmentation performance. We evaluate our method using BRATS 2017 dataset, and the experimental results show excellent performance on the BRATS 2017 validation set.

Paper Details

Date Published: 3 January 2020
PDF: 5 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731B (3 January 2020); doi: 10.1117/12.2557245
Show Author Affiliations
Lihao Qin, Anhui Univ. (China)
Haifeng Zhao, Anhui Univ. (China)
Shaojie Zhang, Anhui Univ. (China)
Zhenyu Tang, Anhui Univ. (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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