Myocardial Iron Loading Assessment by Automatic Left Ventricle Segmentation with Morphological Operations and Geodesic Active Contour on T2∗ images

Yun Gang Luo, Jacky Kl Ko, Lin Shi, Yuefeng Guan, Linong Li, Jing Qin, Pheng Ann Heng, Winnie Cw Chu, Defeng Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Myocardial iron loading thalassemia patients could be identified using T2∗ magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2∗ mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2∗ analysis indicated that regions with intensity lower than 20ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.
Original languageEnglish
Article number12438
JournalScientific Reports
Volume5
DOIs
Publication statusPublished - 28 Jul 2015
Externally publishedYes

ASJC Scopus subject areas

  • General

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