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Computerized Detection of Lesions in Diagnostic Images

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Machine Learning in Radiation Oncology

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Abstract

Computer-aided detection (CADe) has been an active research area in medical imaging. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must read. They may overlook lesions from such a large number of medical images. Consequently, CADe that provides suspicious lesions with radiologists/physicians is developed and becoming indispensable in their decision making to prevent them from overlooking lesions. Machine learning (ML) plays an essential role in CADe, because lesions and organs in medical images may be too complex to be represented accurately by a simple equation; modeling of such complex objects often requires a number of parameters that have to be determined by data. In this chapter, ML techniques used in CADe schemes for lung nodules in chest radiography and thoracic CT and those for the detection of polyps in CT colonography (CTC) are described, which include patch-/pixel-based ML and feature-based (segmented-object-based) ML.

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Acknowledgments

This work would not have been possible without the help and support of countless people. The author is grateful to all members in the Suzuki laboratory, i.e., postdoctoral scholars, computer scientists, visiting scholars/professors, medical students, graduate/undergraduate students, research technicians, research volunteers, and support staff, in the Department of Radiology at the University of Chicago, for their invaluable assistance in the studies, to colleagues and collaborators for their valuable suggestions. CAD technologies, PML technologies, the bone separation technology, and their source code developed at the University of Chicago have been licensed to companies including R2 Technology (Hologic), Riverain Medical (Riverain Technologies), Deus Technology, Median Technologies, AlgoMedica, Mitsubishi Space Software, General Electric, and Toshiba.

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  1. Department of Radiology, The University of Chicago, P-104A, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA

    Kenji Suzuki PhD

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  1. Kenji Suzuki PhD
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  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

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Suzuki, K. (2015). Computerized Detection of Lesions in Diagnostic Images. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_7

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