Mamalakis, M. orcid.org/0000-0002-4276-4119, Garg, P., Nelson, T. et al. (4 more authors) (2023) Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle. Computerized Medical Imaging and Graphics, 103. 102152. ISSN 0895-6111
Abstract
Patients with myocardial infarction are at elevated risk of sudden cardiac death, and scar tissue arising from infarction is known to play a role. The accurate identification of scars therefore is crucial for risk assessment, quantification and guiding interventions. Typically, core scars and grey peripheral zones are identified by radiologists and clinicians based on cardiac late gadolinium enhancement magnetic resonance images (LGE-MRI). Scar regions from LGE-MRI vary in size, shape, heterogeneity, artifacts, and image resolution. Thus, manual segmentation is time consuming, and influenced by the observer's experience (bias effect). We propose a fully automatic framework that develops 3D anatomical models of the left ventricle with border zone and core scar regions that are free from bias effect. Our myocardium (SOCRATIS), border scar and core scar (BZ-SOCRATIS) segmentation pipelines were evaluated using internal and external validation datasets. The automatic myocardium segmentation framework performed a Dice score of 81.9% and 70.0% in the internal and external validation dataset. The automatic scar segmentation pipeline achieved a Dice score of 60.9% for the core scar segmentation and 43.7% for the border zone scar segmentation in the internal dataset and in the external dataset a Dice score of 44.2% for the core scar segmentation and 54.8% for the border scar segmentation respectively. To the best of our knowledge, this is the first study outlining a fully automatic framework to develop 3D anatomical models of the left ventricle with border zone and core scar regions. Our method exhibits high performance without the need for training or tuning in an unseen cohort (unsupervised).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Published by Elsevier Ltd. |
Keywords: | 3D anatomical models; Automatic cardiac segmentation; Border zone segmentation; Core scar segmentation; LGE-MRI; Left ventricle; Machine learning; Humans; Heart Ventricles; Cicatrix; Contrast Media; Gadolinium; Myocardial Infarction; Magnetic Resonance Imaging |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number WELLCOME TRUST (THE) 205188/Z/16/Z MEDICAL RESEARCH COUNCIL MR/M008894/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Aug 2023 09:05 |
Last Modified: | 31 Aug 2023 09:05 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.compmedimag.2022.102152 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202813 |