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Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke

Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segm...

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Autores principales: Zoetmulder, Riaan, Bruggeman, Agnetha A. E., Išgum, Ivana, Gavves, Efstratios, Majoie, Charles B. L. M., Beenen, Ludo F. M., Dippel, Diederik W. J., Boodt, Nikkie, den Hartog, Sanne J., van Doormaal, Pieter J., Cornelissen, Sandra A. P., Roos, Yvo B. W. E. M., Brouwer, Josje, Schonewille, Wouter J., Pirson, Anne F. V., van Zwam, Wim H., van der Leij, Christiaan, Brans, Rutger J. B., van Es, Adriaan C. G. M., Marquering, Henk A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222185/
https://www.ncbi.nlm.nih.gov/pubmed/35741209
http://dx.doi.org/10.3390/diagnostics12061400
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author Zoetmulder, Riaan
Bruggeman, Agnetha A. E.
Išgum, Ivana
Gavves, Efstratios
Majoie, Charles B. L. M.
Beenen, Ludo F. M.
Dippel, Diederik W. J.
Boodt, Nikkie
den Hartog, Sanne J.
van Doormaal, Pieter J.
Cornelissen, Sandra A. P.
Roos, Yvo B. W. E. M.
Brouwer, Josje
Schonewille, Wouter J.
Pirson, Anne F. V.
van Zwam, Wim H.
van der Leij, Christiaan
Brans, Rutger J. B.
van Es, Adriaan C. G. M.
Marquering, Henk A.
author_facet Zoetmulder, Riaan
Bruggeman, Agnetha A. E.
Išgum, Ivana
Gavves, Efstratios
Majoie, Charles B. L. M.
Beenen, Ludo F. M.
Dippel, Diederik W. J.
Boodt, Nikkie
den Hartog, Sanne J.
van Doormaal, Pieter J.
Cornelissen, Sandra A. P.
Roos, Yvo B. W. E. M.
Brouwer, Josje
Schonewille, Wouter J.
Pirson, Anne F. V.
van Zwam, Wim H.
van der Leij, Christiaan
Brans, Rutger J. B.
van Es, Adriaan C. G. M.
Marquering, Henk A.
author_sort Zoetmulder, Riaan
collection PubMed
description Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet). Performance metrics included the intra-class correlation coefficient (ICC) between automated and manually segmented thrombus volumes, the thrombus localization precision and recall, and the Dice coefficient. The majority of the thrombi were localized. Without VBR, Polar-UNet achieved a thrombus localization recall of 0.82, versus 0.78 achieved by BL-UNet. This high recall was accompanied by a low precision of 0.14 and 0.09. VBR improved precision to 0.65 and 0.56 for Polar-UNet and BL-UNet, respectively, with a small reduction in recall to 0.75 and 0.69. The Dice coefficient achieved by Polar-UNet was 0.44, versus 0.38 achieved by BL-UNet with VBR. Both methods achieved ICCs of 0.41 (95% CI: 0.27–0.54). Restricting the VOI to the brainstem improved the thrombus localization precision, recall, and segmentation overlap compared to the benchmark. VBR improved thrombus localization precision but lowered recall.
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spelling pubmed-92221852022-06-24 Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke Zoetmulder, Riaan Bruggeman, Agnetha A. E. Išgum, Ivana Gavves, Efstratios Majoie, Charles B. L. M. Beenen, Ludo F. M. Dippel, Diederik W. J. Boodt, Nikkie den Hartog, Sanne J. van Doormaal, Pieter J. Cornelissen, Sandra A. P. Roos, Yvo B. W. E. M. Brouwer, Josje Schonewille, Wouter J. Pirson, Anne F. V. van Zwam, Wim H. van der Leij, Christiaan Brans, Rutger J. B. van Es, Adriaan C. G. M. Marquering, Henk A. Diagnostics (Basel) Article Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet). Performance metrics included the intra-class correlation coefficient (ICC) between automated and manually segmented thrombus volumes, the thrombus localization precision and recall, and the Dice coefficient. The majority of the thrombi were localized. Without VBR, Polar-UNet achieved a thrombus localization recall of 0.82, versus 0.78 achieved by BL-UNet. This high recall was accompanied by a low precision of 0.14 and 0.09. VBR improved precision to 0.65 and 0.56 for Polar-UNet and BL-UNet, respectively, with a small reduction in recall to 0.75 and 0.69. The Dice coefficient achieved by Polar-UNet was 0.44, versus 0.38 achieved by BL-UNet with VBR. Both methods achieved ICCs of 0.41 (95% CI: 0.27–0.54). Restricting the VOI to the brainstem improved the thrombus localization precision, recall, and segmentation overlap compared to the benchmark. VBR improved thrombus localization precision but lowered recall. MDPI 2022-06-06 /pmc/articles/PMC9222185/ /pubmed/35741209 http://dx.doi.org/10.3390/diagnostics12061400 Text en © 2022 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
Bruggeman, Agnetha A. E.
Išgum, Ivana
Gavves, Efstratios
Majoie, Charles B. L. M.
Beenen, Ludo F. M.
Dippel, Diederik W. J.
Boodt, Nikkie
den Hartog, Sanne J.
van Doormaal, Pieter J.
Cornelissen, Sandra A. P.
Roos, Yvo B. W. E. M.
Brouwer, Josje
Schonewille, Wouter J.
Pirson, Anne F. V.
van Zwam, Wim H.
van der Leij, Christiaan
Brans, Rutger J. B.
van Es, Adriaan C. G. M.
Marquering, Henk A.
Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title_full Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title_fullStr Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title_full_unstemmed Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title_short Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke
title_sort deep-learning-based thrombus localization and segmentation in patients with posterior circulation stroke
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222185/
https://www.ncbi.nlm.nih.gov/pubmed/35741209
http://dx.doi.org/10.3390/diagnostics12061400
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