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Proceedings Paper

Localization of skeletal and aortic landmarks in trauma CT data based on the discriminative generalized Hough transform
Author(s): Cristian Lorenz; Eberhard Hansis; Jürgen Weese; Heike Carolus
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Paper Abstract

Computed tomography is the modality of choice for poly-trauma patients to assess rapidly skeletal and vascular integrity of the whole body. Often several scans with and without contrast medium or with different spatial resolution are acquired. Efficient reading of the resulting extensive set of image data is vital, since it is often time critical to initiate the necessary therapeutic actions. A set of automatically found landmarks can facilitate navigation in the data and enables anatomy oriented viewing. Following this intention, we selected a comprehensive set of 17 skeletal and 5 aortic landmarks. Landmark localization models for the Discriminative Generalized Hough Transform (DGHT) were automatically created based on a set of about 20 training images with ground truth landmark positions. A hierarchical setup with 4 resolution levels was used. Localization results were evaluated on a separate test set, consisting of 50 to 128 images (depending on the landmark) with available ground truth landmark locations. The image data covers a large amount of variability caused by differences of field-of-view, resolution, contrast agent, patient gender and pathologies. The median localization error for the set of aortic landmarks was 14.4 mm and for the set of skeleton landmarks 5.5 mm. Median localization errors for individual landmarks ranged from 3.0 mm to 31.0 mm. The runtime performance for the whole landmark set is about 5s on a typical PC.

Paper Details

Date Published: 21 March 2016
PDF: 10 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978420 (21 March 2016); doi: 10.1117/12.2216251
Show Author Affiliations
Cristian Lorenz, Philips Research (Germany)
Eberhard Hansis, Philips Research (Germany)
Jürgen Weese, Philips Research (Germany)
Heike Carolus, Philips Research (Germany)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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