TY - GEN
T1 - High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
AU - Attar, Rahman
AU - Pereañez, Marco
AU - Gooya, Ali
AU - Albà, Xènia
AU - Zhang, Le
AU - Piechnik, Stefan K.
AU - Neubauer, Stefan
AU - Petersen, Steffen E.
AU - Frangi, Alejandro F.
N1 - Funding Information:
Acknowledgements. R. Attar was funded by the Faculty of Engineering Doctoral Academy Scholarship, University of Sheffield. This work has been partially supported by the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC), and the European Commission through FP7 contract VPH-DARE@IT (FP7-ICT-2011-9-601055) and H2020 Program contract InSilc (H2020-SC1-2017-CNECT-2-777119). The UKB CMR dataset has been provided under UK Biobank Application 2964.
Funding Information:
R. Attar was funded by the Faculty of Engineering Doctoral Academy Scholarship, University of Sheffield. This work has been partially supported by the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical Sciences Research Council (EPSRC), and the European Commission through FP7 contract VPH-DARE@IT (FP7-ICT-2011-9-601055) and H2020 Program contract InSilc (H2020-SC1-2017-CNECT-2-777119). The UKB CMR dataset has been provided under UK Biobank Application 2964.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge, this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework.
AB - The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge, this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework.
UR - http://www.scopus.com/inward/record.url?scp=85064050771&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-12029-0_13
DO - 10.1007/978-3-030-12029-0_13
M3 - Conference contribution
AN - SCOPUS:85064050771
SN - 9783030120283
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 121
BT - Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
A2 - Pop, Mihaela
A2 - Mansi, Tommaso
A2 - Li, Shuo
A2 - Sermesant, Maxime
A2 - Young, Alistair
A2 - Rhode, Kawal
A2 - Zhao, Jichao
A2 - McLeod, Kristin
PB - Springer-Verlag Italia
T2 - 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -