Paper Info
Title
MI-UNet: Multi-Inputs UNet Incorporating Brain Parcellation for Stroke Lesion Segmentation From T1-Weighted Magnetic Resonance Images
Abstract
Stroke is a serious manifestation of various cerebrovascular diseases and one of the most dangerous diseases in the world today. Volume quantification and location detection of chronic stroke lesions provide vital biomarkers for stroke rehabilitation. Recently, deep learning has seen a rapid growth, with a great potential in segmenting medical images. In this work, unlike most deep learning-based segmentation methods utilizing only magnetic resonance (MR) images as the input, we propose and validate a novel stroke lesion segmentation approach named multi-inputs UNet (MI-UNet) that incorporates brain parcellation information, including gray matter (GM), white matter (WM) and lateral ventricle (LV). The brain parcellation is obtained from 3D diffeomorphic registration and is concatenated with the original MR image to form two-channel inputs to the subsequent MI-UNet. Effectiveness of the proposed pipeline is validated using a dataset consisting of 229 T1-weighted MR images. Experiments are conducted via a five-fold cross-validation. The proposed MI-UNet performed significantly better than UNet in both 2D and 3D settings. Our best results obtained by 3D MI-UNet has superior segmentation performance, as measured by the Dice score, Hausdorff distance, average symmetric surface distance, as well as precision, over other state-of-the-art methods.
Year
DOI
Venue
2021
10.1109/JBHI.2020.2996783
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Brain,Humans,Image Processing, Computer-Assisted,Magnetic Resonance Imaging,Stroke
Journal
25
Issue
ISSN
Citations 
2
2168-2194
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yue Zhang118453.93
Jiong Wu251.96
Yilong Liu3344.65
Yifan Chen413.07
X. Wu524926.64
Xiaoying Tang688.79