Mr. Lihao Qin Profile

Lihao Qin

at Anhui Univ

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Proceedings Article | 3 January 2020 Paper

Automated brain tumor segmentation using cascaded bootstrapping model

Lihao Qin, Haifeng Zhao, Shaojie Zhang, Zhenyu Tang

Proceedings Volume 11373, 113731B (2020) https://doi.org/10.1117/12.2557245

KEYWORDS: Tumors, Brain, Image segmentation, Neuroimaging, Magnetic resonance imaging

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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.

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