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Zhao, B.* ; Ren, Y.* ; Yu, Z.* ; Yu, J.* ; Peng, T. ; Zhang, X.Y.*

AUCseg: An automatically unsupervised clustering toolbox for 3D-segmentation of high-grade gliomas in multi-parametric MR images.

Front. Oncol. 11:679952 (2021)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. (https://github.com/Haifengtao/AUCseg).
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Publication type Article: Journal article
Document type Scientific Article
Keywords Mri ; Clustering ; Glioma ; Toolbox ; Unsupervised Segmentation
ISSN (print) / ISBN 2234-943X
e-ISSN 2234-943X
Quellenangaben Volume: 11, Issue: , Pages: , Article Number: 679952 Supplement: ,
Publisher Frontiers
Publishing Place Avenue Du Tribunal Federal 34, Lausanne, Ch-1015, Switzerland
Reviewing status Peer reviewed
Grants Shanghai Science and Technology Commission
Shanghai Center for Brain Science and Brain-Inspired Technology, Fudan University original project
ZJ Lab
Shanghai Municipal Science and Technology Major Project
National Natural Science Foundation of China
Shanghai Science and Technology Committee