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 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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Oktay O,Bai W,Guerrero R,Rajchl M,de Marvao A,O'Regan DP,Cook SA,Heinrich MP,Glocker B,Rueckert Ddoi
10.1109/TMI.2016.2597270subject
Has Abstractpub_date
2017-01-01 00:00:00pages
332-342issue
1eissn
0278-0062issn
1558-254Xjournal_volume
36pub_type
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