계명대학교 의학도서관 Repository

EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke

Metadata Downloads
Author(s)
Hulin KuangBijoy K. MenonSung IL SohnWuQiu
Keimyung Author(s)
Sohn, Sung Il
Department
Dept. of Neurology (신경과학)
Journal Title
Med Image Anal
Issued Date
2021
Volume
70
Keyword
Early infarct segmentationASPECTS scoringNon-contrast CTAcute ischemic stroke
Abstract
Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyper-acute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to segment EI and score Alberta Stroke Program Early CT Score (ASPECTS) simultaneously on baseline non-contrast CT (NCCT) scans of AIS patients. The EIS-Net comprises of a 3D triplet convolutional neural network (T-CNN) for EI segmentation and a multi-region classification network for ASPECTS scoring. T-CNN has triple encoders with original NCCT, mirrored NCCT, and atlas as inputs, as well as one decoder. A comparison disparity block (CDB) is designed to extract and enhance image contexts. In the decoder, a multi-level attention gate module (MAGM) is developed to recalibrate the features of the decoder for both segmentation and classification tasks. Evaluations using a high-quality dataset comprising of baseline NCCT and concomitant diffusion weighted MRI (DWI) as reference standard of 260 patients with AIS show that the proposed EIS-Net can accurately segment EI. The EIS-Net segmented EI volume strongly correlates with EI volume on DWI (r = 0.919), and the mean difference between the two volumes is 8.5 mL. For ASPECTS scoring, the proposed EIS-Net achieves an intraclass correlation coefficient of 0.78 for total 10-point ASPECTS and a kappa of 0.75 for dichotomized ASPECTS (≤4 vs. ≥4). Both EI segmentation and ASPECTS scoring tasks achieve state-of-the-art performances.
Keimyung Author(s)(Kor)
손성일
Publisher
School of Medicine (의과대학)
Citation
Hulin Kuang et al. (2021). EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke. Med Image Anal, 70, 101984. doi: 10.1016/j.media.2021.101984
Type
Article
ISSN
1361-8423
Source
https://www.sciencedirect.com/science/article/abs/pii/S136184152100030X
DOI
10.1016/j.media.2021.101984
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43636
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Neurology (신경과학)
공개 및 라이선스
  • 공개 구분공개
  • 엠바고Forever
파일 목록
  • 관련 파일이 존재하지 않습니다.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.