Abstract
In this paper, a computer–aided diagnosis (CAD) system that retrieves similar cases affected with an interstitial lung disease (ILDs) to assist the radiologist in the diagnosis workup is presented and evaluated. The multimodal inter–case distance measure is based on a set of clinical parameters as well as automatically segmented 3–dimensional regions of lung tissue in high–resolution computed tomography (HRCT) of the chest. A global accuracy of 75.1% of correct matching among five classes of lung tissues as well as a mean average retrieval precision at rank 1 of 71% show that automated lung tissue categorization in HRCT data is complementary to case–based retrieval both from the user’s viewpoint and also on the algorithmic side.
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Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P., Müller, H. (2010). 3D Case–Based Retrieval for Interstitial Lung Diseases. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2009. Lecture Notes in Computer Science, vol 5853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11769-5_4
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DOI: https://doi.org/10.1007/978-3-642-11769-5_4
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