Annotation-free probabilistic atlas learning for robust anatomy detection in CT images

Astrid Franz; Nicole Schadewaldt; Heinrich Schulz; Torbjørn Vik; Lisa Kausch; Jan Modersitzki; Rafael Wiemker; Daniel Bystrov

doi:10.1117/12.2082030

20 March 2015 Annotation-free probabilistic atlas learning for robust anatomy detection in CT images

Astrid Franz, Nicole Schadewaldt, Heinrich Schulz, Torbjørn Vik, Lisa Kausch, Jan Modersitzki, Rafael Wiemker, Daniel Bystrov

Author Affiliations +

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 941338 (2015) https://doi.org/10.1117/12.2082030
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Astrid Franz, Nicole Schadewaldt, Heinrich Schulz, Torbjørn Vik, Lisa Kausch, Jan Modersitzki, Rafael Wiemker, and Daniel Bystrov "Annotation-free probabilistic atlas learning for robust anatomy detection in CT images", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941338 (20 March 2015); https://doi.org/10.1117/12.2082030

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