23 February 2012 Automating the expert consensus paradigm for robust lung tissue classification
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Abstract
Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.
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Srinivasan Rajagopalan, Srinivasan Rajagopalan, Ronald A. Karwoski, Ronald A. Karwoski, Sushravya Raghunath, Sushravya Raghunath, Brian J. Bartholmai, Brian J. Bartholmai, Richard A. Robb, Richard A. Robb, } "Automating the expert consensus paradigm for robust lung tissue classification", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831530 (23 February 2012); doi: 10.1117/12.912009; https://doi.org/10.1117/12.912009
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