TY - JOUR
T1 - PerfU-Net
T2 - Baseline infarct estimation from CT perfusion source data for acute ischemic stroke
AU - de Vries, Lucas
AU - Emmer, Bart J.
AU - Majoie, Charles B. L. M.
AU - Marquering, Henk A.
AU - Gavves, Efstratios
N1 - Funding Information: This work is part of the Artificial Intelligence for Early Imaging Based Patient Selection in Acute Ischemic Stroke (AIRBORNE) project. This project was supported by Top Sector Life Sciences and Health and Nicolab B.V. The Netherlands. Funding Information: This work is part of the Artificial Intelligence for Early Imaging Based Patient Selection in Acute Ischemic Stroke (AIRBORNE) project. This project was supported by Top Sector Life Sciences and Health and Nicolab B.V., The Netherlands . Publisher Copyright: © 2023 The Author(s)
PY - 2023/4/1
Y1 - 2023/4/1
N2 - CT perfusion imaging is commonly used for infarct core quantification in acute ischemic stroke patients. The outcomes and perfusion maps of CT perfusion software, however, show many discrepancies between vendors. We aim to perform infarct core segmentation directly from CT perfusion source data using machine learning, excluding the need to use the perfusion maps from standard CT perfusion software. To this end, we present a symmetry-aware spatio-temporal segmentation model that encodes the micro-perfusion dynamics in the brain, while decoding a static segmentation map for infarct core assessment. Our proposed spatio-temporal PerfU-Net employs an attention module on the skip-connections to match the dimensions of the encoder and decoder. We train and evaluate the method on 94 and 62 scans, respectively, using the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge data. We achieve state-of-the-art results compared to methods that only use CT perfusion source imaging with a Dice score of 0.46. We are almost on par with methods that use perfusion maps from third party software, whilst it is known that there is a large variation in these perfusion maps from various vendors. Moreover, we achieve improved performance compared to simple perfusion map analysis, which is used in clinical practice.
AB - CT perfusion imaging is commonly used for infarct core quantification in acute ischemic stroke patients. The outcomes and perfusion maps of CT perfusion software, however, show many discrepancies between vendors. We aim to perform infarct core segmentation directly from CT perfusion source data using machine learning, excluding the need to use the perfusion maps from standard CT perfusion software. To this end, we present a symmetry-aware spatio-temporal segmentation model that encodes the micro-perfusion dynamics in the brain, while decoding a static segmentation map for infarct core assessment. Our proposed spatio-temporal PerfU-Net employs an attention module on the skip-connections to match the dimensions of the encoder and decoder. We train and evaluate the method on 94 and 62 scans, respectively, using the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge data. We achieve state-of-the-art results compared to methods that only use CT perfusion source imaging with a Dice score of 0.46. We are almost on par with methods that use perfusion maps from third party software, whilst it is known that there is a large variation in these perfusion maps from various vendors. Moreover, we achieve improved performance compared to simple perfusion map analysis, which is used in clinical practice.
KW - Acute ischemic stroke
KW - CT perfusion
KW - Infarct core segmentation
KW - Spatio-temporal attention U-net
UR - http://www.scopus.com/inward/record.url?scp=85147197611&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.media.2023.102749
DO - https://doi.org/10.1016/j.media.2023.102749
M3 - Article
C2 - 36731276
SN - 1361-8415
VL - 85
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 102749
ER -