Please use this identifier to cite or link to this item: https://doi.org/10.1109/TMI.2016.2597270
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dc.titleStratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images
dc.contributor.authorOktay O.
dc.contributor.authorBai W.
dc.contributor.authorGuerrero R.
dc.contributor.authorRajchl M.
dc.contributor.authorDe Marvao A.
dc.contributor.authorO'Regan D.P.
dc.contributor.authorCook S.A.
dc.contributor.authorHeinrich M.P.
dc.contributor.authorGlocker B.
dc.contributor.authorRueckert D.
dc.date.accessioned2019-01-08T09:08:04Z
dc.date.available2019-01-08T09:08:04Z
dc.date.issued2017
dc.identifier.citationOktay O., Bai W., Guerrero R., Rajchl M., De Marvao A., O'Regan D.P., Cook S.A., Heinrich M.P., Glocker B., Rueckert D. (2017). Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images. IEEE Transactions on Medical Imaging 36 (1) : 332-342. ScholarBank@NUS Repository. https://doi.org/10.1109/TMI.2016.2597270
dc.identifier.issn02780062
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/150640
dc.description.abstractAccurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy. © 1982-2012 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.sourceScopus
dc.subjectAutomatic landmark localization
dc.subjectcardiac image analysis
dc.subjectmulti-atlas image segmentation
dc.subjectstratified forests
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1109/TMI.2016.2597270
dc.description.sourcetitleIEEE Transactions on Medical Imaging
dc.description.volume36
dc.description.issue1
dc.description.page332-342
dc.description.codenITMID
dc.grant.idEP/K030523/1
dc.grant.idEP/H046410/1
dc.grant.fundingagencyEPSRC, Engineering and Physical Sciences Research Council
dc.grant.fundingagencyEPSRC, Engineering and Physical Sciences Research Council
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