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Article Dans Une Revue IEEE Journal of Biomedical and Health Informatics Année : 2021

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-ventricular Short-axis Cardiac MR Data

Wufeng Xue
  • Fonction : Auteur
Hao Xu
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Fumin Guo
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Matthew Ng
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Quanzheng Li
Lihong Liu
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Jin Ma
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Elias Grinias
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Yeonggul Jang
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Alejandro Debus
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Shuo Li
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Résumé

Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm
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Dates et versions

hal-03229066 , version 1 (19-05-2021)

Identifiants

Citer

Wufeng Xue, Jiahui Li, Zhiqiang Hu, Eric Kerfoot, James Clough, et al.. Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-ventricular Short-axis Cardiac MR Data. IEEE Journal of Biomedical and Health Informatics, 2021, 25 (9), pp.3541-3553. ⟨10.1109/JBHI.2021.3064353⟩. ⟨hal-03229066⟩
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