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Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning

Authors :
Riaan Zoetmulder
Praneeta R. Konduri
Iris V. Obdeijn
Efstratios Gavves
Ivana Išgum
Charles B.L.M. Majoie
Diederik W.J. Dippel
Yvo B.W.E.M. Roos
Mayank Goyal
Peter J. Mitchell
Bruce C. V. Campbell
Demetrius K. Lopes
Gernot Reimann
Tudor G. Jovin
Jeffrey L. Saver
Keith W. Muir
Phil White
Serge Bracard
Bailiang Chen
Scott Brown
Wouter J. Schonewille
Erik van der Hoeven
Volker Puetz
Henk A. Marquering
Source :
Diagnostics, Vol 11, Iss 9, p 1621 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Final lesion volume (FLV) is a surrogate outcome measure in anterior circulation stroke (ACS). In posterior circulation stroke (PCS), this relation is plausibly understudied due to a lack of methods that automatically quantify FLV. The applicability of deep learning approaches to PCS is limited due to its lower incidence compared to ACS. We evaluated strategies to develop a convolutional neural network (CNN) for PCS lesion segmentation by using image data from both ACS and PCS patients. We included follow-up non-contrast computed tomography scans of 1018 patients with ACS and 107 patients with PCS. To assess whether an ACS lesion segmentation generalizes to PCS, a CNN was trained on ACS data (ACS-CNN). Second, to evaluate the performance of only including PCS patients, a CNN was trained on PCS data. Third, to evaluate the performance when combining the datasets, a CNN was trained on both datasets. Finally, to evaluate the performance of transfer learning, the ACS-CNN was fine-tuned using PCS patients. The transfer learning strategy outperformed the other strategies in volume agreement with an intra-class correlation of 0.88 (95% CI: 0.83–0.92) vs. 0.55 to 0.83 and a lesion detection rate of 87% vs. 41–77 for the other strategies. Hence, transfer learning improved the FLV quantification and detection rate of PCS lesions compared to the other strategies.

Details

Language :
English
ISSN :
20754418 and 14973049
Volume :
11
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.3c1497304964af784c306fdb551f821
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics11091621