Imperial College London
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ProfessorDeclanO'Regan

Faculty of Medicine, Institute of Clinical Sciences

Professor of Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Oktay:2016:10.1109/TMI.2016.2597270,
author = {Oktay, O and Bai, W and Guerrero, R and Rajchl, M and de, Marvao A and O, Regan D and Cook, S and Heinrich, M and Glocker, B and Rueckert, D},
doi = {10.1109/TMI.2016.2597270},
journal = {IEEE Transactions on Medical Imaging},
pages = {332--342},
title = {Stratified decision forests for accurate anatomical landmark localization},
url = {http://dx.doi.org/10.1109/TMI.2016.2597270},
volume = {36},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Oktay,O
AU  - Bai,W
AU  - Guerrero,R
AU  - Rajchl,M
AU  - de,Marvao A
AU  - O,Regan D
AU  - Cook,S
AU  - Heinrich,M
AU  - Glocker,B
AU  - Rueckert,D
DO  - 10.1109/TMI.2016.2597270
EP  - 342
PY  - 2016///
SN  - 0278-0062
SP  - 332
TI  - Stratified decision forests for accurate anatomical landmark localization
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2016.2597270
UR  - http://www.ncbi.nlm.nih.gov/pubmed/28113656
UR  - http://hdl.handle.net/10044/1/38907
VL  - 36
ER  -
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