Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning

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 BrownWouter J. Schonewille, Erik van der Hoeven, Volker Puetz, Henk A. Marquering*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 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.

Original languageEnglish
Article number1621
Number of pages15
JournalDiagnostics
Volume11
Issue number9
DOIs
Publication statusPublished - 4 Sep 2021

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