Precise anatomy localization in CT data by an improved probabilistic tissue type atlas

Astrid Franz; Nicole Schadewaldt; Heinrich Schulz; Torbjørn Vik; Martin Bergtholdt; Daniel Bystrov

doi:10.1117/12.2209036

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21 March 2016 Precise anatomy localization in CT data by an improved probabilistic tissue type atlas

Astrid Franz, Nicole Schadewaldt, Heinrich Schulz, Torbjørn Vik, Martin Bergtholdt, Daniel Bystrov

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978444 (2016) https://doi.org/10.1117/12.2209036
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Automated interpretation of CT scans is an important, clinically relevant area as the number of such scans is increasing rapidly and the interpretation is time consuming. Anatomy localization is an important prerequisite for any such interpretation task. This can be done by image-to-atlas registration, where the atlas serves as a reference space for annotations such as organ probability maps. Tissue type based atlases allow fast and robust processing of arbitrary CT scans. Here we present two methods which significantly improve organ localization based on tissue types. A first problem is the definition of tissue types, which until now is done heuristically based on experience. We present a method to determine suitable tissue types from sample images automatically. A second problem is the restriction of the transformation space: all prior approaches use global affine maps. We present a hierarchical strategy to refine this global affine map. For each organ or region of interest a localized tissue type atlas is computed and used for a subsequent local affine registration step. A three-fold cross validation on 311 CT images with different fields-of-view demonstrates a reduction of the organ localization error by 33%.

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Astrid Franz, Nicole Schadewaldt, Heinrich Schulz, Torbjørn Vik, Martin Bergtholdt, and Daniel Bystrov "Precise anatomy localization in CT data by an improved probabilistic tissue type atlas", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978444 (21 March 2016); https://doi.org/10.1117/12.2209036

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