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Lookup NU author(s): Professor Phil White
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.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 convo-lutional 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.
Author(s): Zoetmulder R, Konduri PR, Obdeijn IV, Gavves E, Isgum I, Majoie CBLM, Dippel DWJ, Roos YBWEM, Goyal M, Mitchell PJ, Campbell BCV, Lopes DK, Reimann G, Jovin TG, Saver JL, Muir KW, White P, Bracard S, Chen B, Brown S, Schonewille WJ, van der Hoeven E, Puetz V, Marquering HA
Publication type: Article
Publication status: Published
Print publication date: 01/09/2021
Online publication date: 04/09/2021
Acceptance date: 30/08/2021
Date deposited: 22/09/2021
ISSN (electronic): 2075-4418
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