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Med Image Anal. 2018 Jan;43:23-36. doi: 10.1016/j.media.2017.09.003. Epub 2017 Sep 21.

Integrating geometric configuration and appearance information into a unified framework for anatomical landmark localization.

Author information

1
Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria; Institute for Computer Graphics and Vision, Graz University of Technology, Austria; BioTechMed-Graz, Austria.
2
Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria.
3
Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria; Institute for Computer Graphics and Vision, Graz University of Technology, Austria. Electronic address: stern@icg.tugraz.at.

Abstract

In approaches for automatic localization of multiple anatomical landmarks, disambiguation of locally similar structures as obtained by locally accurate candidate generation is often performed by solely including high level knowledge about geometric landmark configuration. In our novel localization approach, we propose to combine both image appearance information and geometric landmark configuration into a unified random forest framework integrated into an optimization procedure that iteratively refines joint landmark predictions by using the coordinate descent algorithm. Depending on how strong multiple landmarks are correlated in a specific localization task, this integration has the benefit that it remains flexible in deciding whether appearance information or the geometric configuration of multiple landmarks is the stronger cue for solving a localization problem both accurately and robustly. Furthermore, no preliminary choice on how to encode a graphical model describing landmark configuration has to be made. In an extensive evaluation on five challenging datasets involving different 2D and 3D imaging modalities, we show that our proposed method is widely applicable and delivers state-of-the-art results when compared to various other related methods.

KEYWORDS:

Anatomical landmarks; Coordinate descent; Localization; Random regression forest

PMID:
28963961
DOI:
10.1016/j.media.2017.09.003
[Indexed for MEDLINE]
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