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

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...

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Autores principales: Zoetmulder, Riaan, Konduri, Praneeta R., Obdeijn, Iris V., Gavves, Efstratios, Išgum, Ivana, Majoie, Charles B.L.M., Dippel, Diederik W.J., Roos, Yvo B.W.E.M., Goyal, Mayank, Mitchell, Peter J., Campbell, Bruce C. V., Lopes, Demetrius K., Reimann, Gernot, Jovin, Tudor G., Saver, Jeffrey L., Muir, Keith W., White, Phil, Bracard, Serge, Chen, Bailiang, Brown, Scott, Schonewille, Wouter J., van der Hoeven, Erik, Puetz, Volker, Marquering, Henk A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466415/
https://www.ncbi.nlm.nih.gov/pubmed/34573963
http://dx.doi.org/10.3390/diagnostics11091621
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author Zoetmulder, Riaan
Konduri, Praneeta R.
Obdeijn, Iris V.
Gavves, Efstratios
Išgum, Ivana
Majoie, Charles B.L.M.
Dippel, Diederik W.J.
Roos, Yvo B.W.E.M.
Goyal, Mayank
Mitchell, Peter J.
Campbell, Bruce C. V.
Lopes, Demetrius K.
Reimann, Gernot
Jovin, Tudor G.
Saver, Jeffrey L.
Muir, Keith W.
White, Phil
Bracard, Serge
Chen, Bailiang
Brown, Scott
Schonewille, Wouter J.
van der Hoeven, Erik
Puetz, Volker
Marquering, Henk A.
author_facet Zoetmulder, Riaan
Konduri, Praneeta R.
Obdeijn, Iris V.
Gavves, Efstratios
Išgum, Ivana
Majoie, Charles B.L.M.
Dippel, Diederik W.J.
Roos, Yvo B.W.E.M.
Goyal, Mayank
Mitchell, Peter J.
Campbell, Bruce C. V.
Lopes, Demetrius K.
Reimann, Gernot
Jovin, Tudor G.
Saver, Jeffrey L.
Muir, Keith W.
White, Phil
Bracard, Serge
Chen, Bailiang
Brown, Scott
Schonewille, Wouter J.
van der Hoeven, Erik
Puetz, Volker
Marquering, Henk A.
author_sort Zoetmulder, Riaan
collection PubMed
description 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.
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spelling pubmed-84664152021-09-27 Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning Zoetmulder, Riaan Konduri, Praneeta R. Obdeijn, Iris V. Gavves, Efstratios Išgum, Ivana Majoie, Charles B.L.M. Dippel, Diederik W.J. Roos, Yvo B.W.E.M. Goyal, Mayank Mitchell, Peter J. Campbell, Bruce C. V. Lopes, Demetrius K. Reimann, Gernot Jovin, Tudor G. Saver, Jeffrey L. Muir, Keith W. White, Phil Bracard, Serge Chen, Bailiang Brown, Scott Schonewille, Wouter J. van der Hoeven, Erik Puetz, Volker Marquering, Henk A. Diagnostics (Basel) Article 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. MDPI 2021-09-04 /pmc/articles/PMC8466415/ /pubmed/34573963 http://dx.doi.org/10.3390/diagnostics11091621 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zoetmulder, Riaan
Konduri, Praneeta R.
Obdeijn, Iris V.
Gavves, Efstratios
Išgum, Ivana
Majoie, Charles B.L.M.
Dippel, Diederik W.J.
Roos, Yvo B.W.E.M.
Goyal, Mayank
Mitchell, Peter J.
Campbell, Bruce C. V.
Lopes, Demetrius K.
Reimann, Gernot
Jovin, Tudor G.
Saver, Jeffrey L.
Muir, Keith W.
White, Phil
Bracard, Serge
Chen, Bailiang
Brown, Scott
Schonewille, Wouter J.
van der Hoeven, Erik
Puetz, Volker
Marquering, Henk A.
Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title_full Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title_fullStr Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title_full_unstemmed Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title_short Automated Final Lesion Segmentation in Posterior Circulation Acute Ischemic Stroke Using Deep Learning
title_sort automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466415/
https://www.ncbi.nlm.nih.gov/pubmed/34573963
http://dx.doi.org/10.3390/diagnostics11091621
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