Semi-Supervised Learning in Medical Image Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation Using FLAIR Image
ZunHyan Rieu, Donghyeon Kim, Jee Young Kim, Regina E. Y. Kim, Minho Lee, Min Kyoung Lee, Se Won Oh, Sheng‐Min Wang, Nak-Young Kim, Dong Woo Kang, Hyun Kook Lim · Preprints.org · 2021
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.