Semi-Supervised Learning in Medical Image Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR Image

ZunHyan Rieu; Donghyeon Kim; JeeYoung Kim; Regina EY Kim; Minho Lee; Min Kyoung Lee; Se Won Oh; Sheng-Min Wang; Nak-Young Kim; Dong Woo Kang; Hyun Kook Lim

doi:10.20944/preprints202104.0004.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 31 March 2021 / Approved: 1 April 2021 / Online: 1 April 2021 (10:01:05 CEST)

White matter hyperintensity (WMH) has been considered the primary biomarker from small-vessel cerebrovascular disease to Alzheimer’s disease (AD) and has been reported for its correlation of brain structural changes. To perform WMH related analysis with brain structure, both T1-weighted (T1w) and (Fluid Attenuated Inversion Recovery(FLAIR) are required. However, in a clinical situation, it is limited to obtain 3D T1w and FLAIR images simultaneously. Also, the most of brain segmentation technique supports 3D T1w only. Therefore, we introduced the semi-supervised learning method that can perform brain segmentation using FLAIR image only. Our method achieved a dice overlap score of 0.86 for brain tissue segmentation on FLAIR, with the relative volume difference between T1w and FLAIR segmentation under 4.8%, which is just as reliable as the segmentation done by its paired T1w image. We believe our semi-supervised learning method has a great potential to be used to other MRI sequences and provide encouragement to people who seek brain tissue segmentation from a non-T1w image.

segmentation; deep-learning; FLAIR; T1w; white matter hyperintensity

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Semi-Supervised Learning in Medical Image Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR Image

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