Automatic aortic valve landmark localization in coronary CT angiography using colonial walk

Walid Abdullah Al, Ho Yub Jung, Il Dong Yun, Yeonggul Jang, Hyung Bok Park, Hyuk Jae Chang

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation. In order to examine the valve and mark the landmarks, physicians prefer a view parallel to the hinge plane, instead of using the conventional axial, coronal or sagittal view. However, customizing the view is a difficult and time-consuming task because of unclear aorta pose and different artifacts of CCTA. Therefore, automatic localization of landmarks can serve as a useful guide to the physicians customizing the viewpoint. In this paper, we present an automatic method to localize the aortic valve landmarks using colonial walk, a regression tree-based machine-learning algorithm. For efficient learning from the training set, we propose a two-phase optimized search space learning model in which a representative point inside the valvular area is first learned from the whole CT volume. All eight landmarks are then learned from a smaller area around that point. Experiment with preprocedural CCTA images of TAVI undergoing patients showed that our method is robust under high stenotic variation and notably efficient, as it requires only 12 milliseconds to localize all eight landmarks, as tested on a 3.60 GHz single-core CPU.

Original languageEnglish
Article numbere0200317
JournalPloS one
Volume13
Issue number7
DOIs
Publication statusPublished - 2018 Jul

Bibliographical note

Funding Information:
The data collection, study design, analysis, and decision publish are supported by Institute for Information and communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.R0101-15-0171, Development of Multi-modality Imaging and 3D Simulation-Based Integrative Diagnosis-Treatment Support Software System for Cardiovascular Diseases) Following grants supported in preparation of the manuscript. This research was supported by MIST (Ministry of Science & ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & Communications Technology Promotion) (2017-0-00137). Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, Technology (No. 2017R1A2B4004503 and 2014M3C7A1046050), Hankuk University of Foreign Studies Research Fund of 2018. The data collection, study design, analysis, and decision publish are supported by Institute for Information and communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.R0101-15-0171, Development of Multi-modality Imaging and 3D Simulation-Based Integrative Diagnosis-Treatment Support Software System for Cardiovascular Diseases). Following grants supported in preparation of the manuscript. This research was supported by MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information and Communications Technology Promotion) (2017-0-00137). Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, Technology (No. 2017R1A2B4004503), Hankuk University of Foreign Studies Research Fund of 2018.

Publisher Copyright:
© 2018 Al et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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