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

Laks Raghupathi; MS Dinesh; Pandu R. Devarakota; Gerardo Hermosillo Valadez; Matthias Wolf

doi:10.1117/12.2007929

18 March 2013 A method for automatic matching of multi-timepoint findings for enhanced clinical workflow

Laks Raghupathi, MS Dinesh, Pandu R. Devarakota, Gerardo Hermosillo Valadez, Matthias Wolf

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86700I (2013) https://doi.org/10.1117/12.2007929
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Laks Raghupathi, MS Dinesh, Pandu R. Devarakota, Gerardo Hermosillo Valadez, and Matthias Wolf "A method for automatic matching of multi-timepoint findings for enhanced clinical workflow", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700I (18 March 2013); https://doi.org/10.1117/12.2007929

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KEYWORDS

Image registration

Lung

Computer aided design

Feature selection

Machine learning

Computed tomography

CAD systems

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