Toward knowledge-enhanced viewing using encyclopedias and model-based segmentation

Reinhard Kneser; Helko Lehmann; Dieter Geller; Yue-Chen Qian; Jürgen Weese

doi:10.1117/12.811587

27 February 2009 Toward knowledge-enhanced viewing using encyclopedias and model-based segmentation

Reinhard Kneser, Helko Lehmann, Dieter Geller, Yue-Chen Qian, Jürgen Weese

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72601D (2009) https://doi.org/10.1117/12.811587
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

To make accurate decisions based on imaging data, radiologists must associate the viewed imaging data with the corresponding anatomical structures. Furthermore, given a disease hypothesis possible image findings which verify the hypothesis must be considered and where and how they are expressed in the viewed images. If rare anatomical variants, rare pathologies, unfamiliar protocols, or ambiguous findings are present, external knowledge sources such as medical encyclopedias are consulted. These sources are accessed using keywords typically describing anatomical structures, image findings, pathologies. In this paper we present our vision of how a patient's imaging data can be automatically enhanced with anatomical knowledge as well as knowledge about image findings. On one hand, we propose the automatic annotation of the images with labels from a standard anatomical ontology. These labels are used as keywords for a medical encyclopedia such as STATdx to access anatomical descriptions, information about pathologies and image findings. On the other hand we envision encyclopedias to contain links to region- and finding-specific image processing algorithms. Then a finding is evaluated on an image by applying the respective algorithm in the associated anatomical region. Towards realization of our vision, we present our method and results of automatic annotation of anatomical structures in 3D MRI brain images. Thereby we develop a complex surface mesh model incorporating major structures of the brain and a model-based segmentation method. We demonstrate the validity by analyzing the results of several training and segmentation experiments with clinical data focusing particularly on the visual pathway.

Citation Download Citation

Reinhard Kneser, Helko Lehmann, Dieter Geller, Yue-Chen Qian, and Jürgen Weese "Toward knowledge-enhanced viewing using encyclopedias and model-based segmentation", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72601D (27 February 2009); https://doi.org/10.1117/12.811587

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