TY - CHAP
T1 - Whole Heart Anatomical Refinement from CCTA Using Extrapolation and Parcellation
AU - Xu, Hao
AU - Niederer, Steven A.
AU - Williams, Steven E.
AU - Newby, David E.
AU - Williams, Michelle C.
AU - Young, Alistair A.
N1 - Funding Information:
Acknowledgements. This research was supported by the UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, and core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. We thank Siemens Healthineers for allowing us to use the AXseg prototypical software during this research. SCOT-HEART was funded by The Chief Scientist Office of the Scottish Government Health and Social Care Directorates (CZH/4/588), with supplementary awards from Edinburgh and Lothian’s Health Foundation Trust and the Heart Diseases Research Fund. MCW and DEN are supported by the British Heart Foundation FS/ICRF/20/26002 and CH/09/002.
Funding Information:
This research was supported by the UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare, and core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z]. We thank Siemens Healthineers for allowing us to use the AXseg prototypical software during this research. SCOT-HEART was funded by The Chief Scientist Office of the Scottish Government Health and Social Care Directorates (CZH/4/588), with supplementary awards from Edinburgh and Lothian?s Health Foundation Trust and the Heart Diseases Research Fund. MCW and DEN are supported by the British Heart Foundation FS/ICRF/20/26002 and CH/09/002.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Coronary computed tomography angiography (CCTA) provides detailed anatomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcellation, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com.
AB - Coronary computed tomography angiography (CCTA) provides detailed anatomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcellation, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com.
KW - CCTA
KW - U-Net
KW - Whole heart segmentation
UR - http://www.scopus.com/inward/record.url?scp=85111801389&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78710-3_7
DO - 10.1007/978-3-030-78710-3_7
M3 - Conference paper
AN - SCOPUS:85111801389
SN - 9783030787097
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 63
EP - 70
BT - Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
A2 - Ennis, Daniel B.
A2 - Perotti, Luigi E.
A2 - Wang, Vicky Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
Y2 - 21 June 2021 through 25 June 2021
ER -