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Computer-Aided Diagnosis Systems for Acute Renal Transplant Rejection: Challenges and Methodologies

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Abstract

This chapter overviews one of the most critical problems in urology, namely detection of acute renal transplant rejection. Developing an effective, fast, and accurate computer-aided diagnosis (CAD) system for early detection of acute renal rejection is of great clinical importance for the management of these patients. For this reason, CAD systems for early detection of renal transplant rejection have been investigated in a huge number of research studies using different image modalities, such as ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), and radionuclide imaging. A typical CAD system for kidney diagnosis consists of a set of processing steps including, but not limited to, image registration to account for kidney motion, segmentation of the kidney and/or its compartments (e.g., cortex, medulla), construction of agent kinetic curves, functional parameters estimation, and diagnosis and assessment of the kidney status. Due to the widespread popularity of US and MRI, this chapter overviews the current state-of-the-art CAD systems that have been developed for kidney diagnosis using these two image modalities. In addition, the chapter addresses several challenges that researchers face in developing efficient, fast, and reliable CAD systems for early detection of kidney diseases.

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Authors and Affiliations

  1. BioImaging laboratory, Bioengineering Department and with the Electrical and Computer Engineering Department, University of Louisville, Louisville, KY, 40292, USA

    Mahmoud Mostapha, Fahmi Khalifa, Amir Alansary & Ahmed Soliman

  2. Biomedical Technologies, Inc., Roseville, CA, USA

    Jasjit Suri

  3. BioImaging laboratory, Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA

    Ayman S. El-Baz

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  1. Mahmoud Mostapha
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  2. Fahmi Khalifa
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  3. Amir Alansary
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  4. Ahmed Soliman
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  5. Jasjit Suri
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  6. Ayman S. El-Baz
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Correspondence to Ayman S. El-Baz .

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  1. Department of Bioengineering, University of Louisville, Louisville, Kentucky, USA

    Ayman S. El-Baz

  2. University of Cagliari, Cagliari, Italy

    Luca Saba

  3. Biomedical Technologies, Inc Idaho State University (Affiliated), Roseville, California, USA

    Jasjit Suri

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Mostapha, M., Khalifa, F., Alansary, A., Soliman, A., Suri, J., El-Baz, A.S. (2014). Computer-Aided Diagnosis Systems for Acute Renal Transplant Rejection: Challenges and Methodologies. In: El-Baz, A., Saba, L., Suri, J. (eds) Abdomen and Thoracic Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8498-1_1

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