CAD-system based on kinetic analysis for non-mass-enhancing lesions in DCE-MRI

Sebastian Goebl; Claudia Plant; Marc Lobbes; Anke Meyer-Bäse

doi:10.1117/12.2019276

29 May 2013 CAD-system based on kinetic analysis for non-mass-enhancing lesions in DCE-MRI

Sebastian Goebl, Claudia Plant, Marc Lobbes, Anke Meyer-Bäse

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Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 87500R (2013) https://doi.org/10.1117/12.2019276
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Non-mass enhancing lesions represent one of the most challenging types of lesions for both the clinician as well as current computer-aided diagnosis (CAD) systems. Differently from the well-studied mass-enhancing tumors these lesions do not exhibit a typical kinetic behavior that can be further easily categorized into benign or malignant based on feature descriptors. Furthermore, the poorly defined tumor borders pose a difficulty to even the most sophisticated segmentation algorithms. To address these challenges in terms of segmentation and atypical contrast enhancement dynamics, we apply an ICA-based segmentation on these lesions and extract from the average signal intensity curve of the most representative independent component (IC). Subsequently the dynamics of this IC is modeled based on mathematical models such as the empirical mathematical model and the phenomenological universalities. An automated computer-aided diagnosis system evaluates the atypical behavior of these lesions, and additionally compares the benefit of ICA-segmentation versus active contour segmentation.

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Sebastian Goebl, Sebastian Goebl, Claudia Plant, Claudia Plant, Marc Lobbes, Marc Lobbes, Anke Meyer-Bäse, Anke Meyer-Bäse, "CAD-system based on kinetic analysis for non-mass-enhancing lesions in DCE-MRI", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500R (29 May 2013); doi: 10.1117/12.2019276; https://doi.org/10.1117/12.2019276

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