학술논문
Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
Document Type
article
Author
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)
Subject
Language
English
ISSN
2075-4418
14973049
14973049
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.