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

Wufeng Xue, Jiahui Li, Zhiqiang Hu, Eric Kerfoot, James Clough, Ilkay Oksuz, Hao Xu, Vicente Grau, Fumin Guo, Matthew Ng, Xiang Li, Quanzheng Li, Lihong Liu, Jin Ma, Elias Grinias, Georgios Tziritas, Wenjun Yan, Angelica Atehortua, Mireille Garreau, Yeonggul JangAlejandro Debus, Enzo Ferrante, Guanyu Yang, Tiancong Hua, Shuo Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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$^2$ for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.

Original languageEnglish
Article number9372751
Pages (from-to)3541-3553
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume25
Issue number9
DOIs
Publication statusPublished - Sept 2021

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Deep neural network
  • left ventricle
  • quantification
  • regression
  • segmentation

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