Brain SegNet: 3D local refinement network for brain lesion segmentation.

Hu, Xiaojun; Luo, Weijian; Hu, Jiliang; Guo, Sheng; Huang, Weilin; Scott, Matthew R; Wiest, Roland; Dahlweid, Michael; Reyes, Mauricio (2020). Brain SegNet: 3D local refinement network for brain lesion segmentation. BMC medical imaging, 20(1), p. 17. BioMed Central 10.1186/s12880-020-0409-2

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MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Wiest, Roland and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1471-2342

Publisher:

BioMed Central

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

04 Mar 2020 09:46

Last Modified:

04 Mar 2020 09:50

Publisher DOI:

10.1186/s12880-020-0409-2

PubMed ID:

32046685

Uncontrolled Keywords:

3D brain MRIs Brain tumor segmentation Curriculum learning Stroke outcome prediction

BORIS DOI:

10.7892/boris.140719

URI:

https://boris.unibe.ch/id/eprint/140719

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