Automatic development of 3D anatomical models of border zone and core scar regions in the left ventricle. (January 2023)

• Record Type: Journal Article
• Authors: Mamalakis, Michail; Garg, Pankaj; Nelson, Tom; Lee, Justin; Swift, Andrew J.; Wild, James M.; Clayton, Richard H.
• Is Part Of: Computerized medical imaging and graphics. Volume 103(2023)
• Publication Date: 2023-01
• Subjects: LGE-MRI -- Automatic cardiac segmentation -- Machine learning -- Border zone segmentation -- Core scar segmentation -- Left ventricle -- 3D anatomical models; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102152
• Languages: English
• ISSNs: 0895-6111

Abstract

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). Graphical abstract: Highlights: An automatic framework of 3D anatomical models of the myocardium, border and core scar of the left ventricle. An automatic unsupervised border and core scar segmentation pipeline. A protocol to evaluate automatic pipelines, the importance of unbiased cohorts. A verification that robust myocardium segmentation results increases the performance of scar segmentation.