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Proceedings Paper

A method for automatic matching of multi-timepoint findings for enhanced clinical workflow
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Paper Abstract

Non-interventional diagnostics (CT or MR) enables early identification of diseases like cancer. Often, lesion growth assessment done during follow-up is used to distinguish between benign and malignant ones. Thus correspondences need to be found for lesions localized at each time point. Manually matching the radiological findings can be time consuming as well as tedious due to possible differences in orientation and position between scans. Also, the complicated nature of the disease makes the physicians to rely on multiple modalities (PETCT, PET-MR) where it is even more challenging. Here, we propose an automatic feature-based matching that is robust to change in organ volume, subpar or no registration that can be done with very less computations. Traditional matching methods rely mostly on accurate image registration and applying the resulting deformation map on the findings coordinates. This has disadvantages when accurate registration is time-consuming or may not be possible due to vast organ volume differences between scans. Our novel matching proposes supervised learning by taking advantage of the underlying CAD features that are already present and considering the matching as a classification problem. In addition, the matching can be done extremely fast and at reasonable accuracy even when the image registration fails for some reason. Experimental results on real-world multi-time point thoracic CT data showed an accuracy of above 90% with negligible false positives on a variety of registration scenarios.

Paper Details

Date Published: 18 March 2013
PDF: 10 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700I (18 March 2013); doi: 10.1117/12.2007929
Show Author Affiliations
Laks Raghupathi, Siemens Corp., Corporate Technology (India)
MS Dinesh, Siemens Corp., Corporate Technology (India)
Pandu R. Devarakota, Siemens Corp., Corporate Technology (India)
Gerardo Hermosillo Valadez, Siemens Healthcare, Inc. (United States)
Matthias Wolf, Siemens Healthcare, Inc. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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