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Proceedings Paper

Annotation-free probabilistic atlas learning for robust anatomy detection in CT images
Author(s): Astrid Franz; Nicole Schadewaldt; Heinrich Schulz; Torbjørn Vik; Lisa Kausch; Jan Modersitzki; Rafael Wiemker; Daniel Bystrov
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Paper Abstract

A fully automatic method generating a whole body atlas from CT images is presented. The atlas serves as a reference space for annotations. It is based on a large collection of partially overlapping medical images and a registration scheme. The atlas itself consists of probabilistic tissue type maps and can represent anatomical variations. The registration scheme is based on an entropy-like measure of these maps and is robust with respect to field-of-view variations. In contrast to other atlas generation methods, which typically rely on a sufficiently large set of annotations on training cases, the presented method requires only the images. An iterative refinement strategy is used to automatically stitch the images to build the atlas.

Affine registration of unseen CT images to the probabilistic atlas can be used to transfer reference annotations, e.g. organ models for segmentation initialization or reference bounding boxes for field-of-view selection. The robustness and generality of the method is shown using a three-fold cross-validation of the registration on a set of 316 CT images of unknown content and large anatomical variability. As an example, 17 organs are annotated in the atlas reference space and their localization in the test images is evaluated. The method yields a recall (sensitivity), specificity and precision of at least 96% and thus performs excellent in comparison to competitors.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941338 (20 March 2015); doi: 10.1117/12.2082030
Show Author Affiliations
Astrid Franz, Philips Research (Germany)
Nicole Schadewaldt, Philips Research (Germany)
Heinrich Schulz, Philips Research (Germany)
Torbjørn Vik, Philips Research (Germany)
Lisa Kausch, Univ. zu Lübeck (Germany)
Jan Modersitzki, Univ. zu Lübeck (Germany)
Rafael Wiemker, Philips Research (Germany)
Daniel Bystrov, Philips Research (Germany)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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