Mostrar el registro sencillo del ítem
dc.contributor.author
Xue, Wufeng
dc.contributor.author
Li, Jiahui
dc.contributor.author
Hu, Zhiqiang
dc.contributor.author
Kerfoot, Eric
dc.contributor.author
Clough, James
dc.contributor.author
Oksuz, Ilkay
dc.contributor.author
Xu, Hao
dc.contributor.author
Grau, Vicente
dc.contributor.author
Guo, Fumin
dc.contributor.author
Ng, Matthew
dc.contributor.author
Li, Xiang
dc.contributor.author
Li, Quanzheng
dc.contributor.author
Liu, Lihong
dc.contributor.author
Ma, Jin
dc.contributor.author
Grinias, Elias
dc.contributor.author
Tziritas, Georgios
dc.contributor.author
Yan, Wenjun
dc.contributor.author
Atehortua, Angelica
dc.contributor.author
Garreau, Mireille
dc.contributor.author
Jang, Yeonggul
dc.contributor.author
Debus, Alejandro
dc.contributor.author
Ferrante, Enzo
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Yang, Guanyu
dc.contributor.author
Hua, Tiancong
dc.contributor.author
Li, Shuo
dc.date.available
2023-01-12T09:29:44Z
dc.date.issued
2021-09
dc.identifier.citation
Xue, Wufeng; Li, Jiahui; Hu, Zhiqiang; Kerfoot, Eric; Clough, James; et al.; Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data; Institute of Electrical and Electronics Engineers; IEEE Journal of Biomedical and Health Informatics; 25; 9; 9-2021; 3541-3553
dc.identifier.issn
2168-2194
dc.identifier.uri
http://hdl.handle.net/11336/184438
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DEEP NEURAL NETWORK
dc.subject
LEFT VENTRICLE
dc.subject
QUANTIFICATION
dc.subject
REGRESSION
dc.subject
SEGMENTATION
dc.subject.classification
Ciencias de la Información y Bioinformática
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
Ciencias de la Computación e Información
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.title
Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-10-28T19:19:08Z
dc.identifier.eissn
2168-2208
dc.journal.volume
25
dc.journal.number
9
dc.journal.pagination
3541-3553
dc.journal.pais
Estados Unidos
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.description.fil
Fil: Xue, Wufeng. Shenzhen University; China. Western University; Canadá
dc.description.fil
Fil: Li, Jiahui. Beijing University Of Posts And Telecommunications; China
dc.description.fil
Fil: Hu, Zhiqiang. Peking University; China
dc.description.fil
Fil: Kerfoot, Eric. King's College; Reino Unido
dc.description.fil
Fil: Clough, James. King's College; Reino Unido
dc.description.fil
Fil: Oksuz, Ilkay. King's College; Reino Unido
dc.description.fil
Fil: Xu, Hao. University of Oxford; Reino Unido
dc.description.fil
Fil: Grau, Vicente. University of Oxford; Reino Unido
dc.description.fil
Fil: Guo, Fumin. University of Toronto; Canadá
dc.description.fil
Fil: Ng, Matthew. University of Toronto; Canadá
dc.description.fil
Fil: Li, Xiang. Massachusetts General Hospital; Estados Unidos
dc.description.fil
Fil: Li, Quanzheng. Massachusetts General Hospital; Estados Unidos
dc.description.fil
Fil: Liu, Lihong. No especifíca;
dc.description.fil
Fil: Ma, Jin. No especifíca;
dc.description.fil
Fil: Grinias, Elias. No especifíca;
dc.description.fil
Fil: Tziritas, Georgios. No especifíca;
dc.description.fil
Fil: Yan, Wenjun. Fudan University; China
dc.description.fil
Fil: Atehortua, Angelica. No especifíca;
dc.description.fil
Fil: Garreau, Mireille. No especifíca;
dc.description.fil
Fil: Jang, Yeonggul. No especifíca;
dc.description.fil
Fil: Debus, Alejandro. No especifíca;
dc.description.fil
Fil: Ferrante, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
dc.description.fil
Fil: Yang, Guanyu. Southeast University; China
dc.description.fil
Fil: Hua, Tiancong. Southeast University; China
dc.description.fil
Fil: Li, Shuo. Western University; Canadá
dc.journal.title
IEEE Journal of Biomedical and Health Informatics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/JBHI.2021.3064353
Archivos asociados