Stratified decision forests for accurate anatomical landmark localization in cardiac images

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Title: Stratified decision forests for accurate anatomical landmark localization in cardiac images
Authors: Oktay, O
Bai, W
Guerrero, R
Rajchl, M
De Marvao, A
O'Regan, D
Cook, S
Heinrich, M
Glocker, B
Rueckert, D
Item Type: Journal Article
Abstract: Accurate 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 highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart 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.
Issue Date: 13-Sep-2016
Date of Acceptance: 27-Jul-2016
URI: http://hdl.handle.net/10044/1/38907
DOI: https://dx.doi.org/10.1109/TMI.2016.2597270
ISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Start Page: 332
End Page: 342
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 36
Issue: 1
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted.This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: National Institute for Health Research
Engineering & Physical Science Research Council (EPSRC)
British Heart Foundation
Funder's Grant Number: RDB02 79560
EP/K030523/1
PG/12/27/29489
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Automatic landmark localization
cardiac image analysis
multi-atlas image segmentation
stratified forests
REGRESSION FORESTS
MR-IMAGES
VENTRICLE SEGMENTATION
REGISTRATION
EFFICIENT
HEART
FEATURES
FUSION
MODEL
08 Information And Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Appears in Collections:Faculty of Engineering
Computing
Clinical Sciences
Imaging Sciences
National Heart and Lung Institute
Molecular Sciences
Department of Medicine
Faculty of Medicine



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