Abstract
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.
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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.
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Xu, H., Niederer, S.A., Williams, S.E., Newby, D.E., Williams, M.C., Young, A.A. (2021). Whole Heart Anatomical Refinement from CCTA Using Extrapolation and Parcellation. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_7
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DOI: https://doi.org/10.1007/978-3-030-78710-3_7
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