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Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography

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Computational Intelligence in Biomedical Imaging

Abstract

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of tissue. This chapter highlights our recent contributions to abdominal multi-organ analysis employing constraints typical to medical images and adapted to patient data. A new formulation for graph-based methods to segment abdominal organs from multi-phase CT data is first presented. The method extends basic graph cuts by using: multi-phases enhancement modeling, shape priors and location constraints. The multi-organ localization is also addressed using maximum a posteriori (MAP) probability estimations of organs’ location, orientation, and scale. The probabilistic framework models the inter-organ spatial relations using a minimum volume overlap constraint. The liver, spleen, left kidney, right kidney and pancreas are concomitantly analyzed in the multi-organ analysis framework. Finally, the automated detection and segmentation of abdominal tumors (i.e., hepatic tumors) from abdominal CT images is presented using once again shape and enhancement constraints. Features are computed for the tumor candidates and machine learning is used to select the optimal features to separate true and false detections. The methods illustrate multi-scale analyses of the abdomen, from multi-organ to organ and tumors and promise to support the processing of large medical data in the clinically oriented integrated analysis of the abdomen.

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Acknowledgments

This work was supported in part by the Intramural Research Program of the National Institutes of Health, Clinical Center. The authors would like to acknowledge the contributions of (in alphabetical order) Ananda S. Chowdhury, Zhixi Li, Xiaofeng Liu, Vivek Pamulapati, John A. Pura,William J. Richbrough, Jesse K. Sandberg, Shijun Wang, Jeremy M. Watt, and Jianhua Yao.

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Authors and Affiliations

  1. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Medical Center, 111 Michigan Avenue NW, Washington, DC, 20010, USA

    Marius George Linguraru

  2. Imaging Biomarkers and Computer Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892, USA

    Ronald M. Summers

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  1. Marius George Linguraru
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Correspondence to Marius George Linguraru .

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Editors and Affiliations

  1. Department of Radiology, University of Chicago, Chicago, Illinois, USA

    Kenji Suzuki

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Linguraru, M.G., Summers, R.M. (2014). Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_5

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