Automatic classification of lung tumour heterogeneity according to a visual-based score system in dynamic contrast enhanced CT sequences

Alessandro Bevilacqua and Serena Baiocco ()
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Alessandro Bevilacqua: Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, Bologna 40136, Italy
Serena Baiocco: Advanced Research Centre on Electronic Systems, University of Bologna, Via Toffano 2/2 Bologna 40125, Italy

International Journal of Modern Physics C (IJMPC), 2016, vol. 27, issue 09, 1-14

Abstract: Computed tomography (CT) technologies have been considered for a long time as one of the most effective medical imaging tools for morphological analysis of body parts. Contrast Enhanced CT (CE-CT) also allows emphasising details of tissue structures whose heterogeneity, inspected through visual analysis, conveys crucial information regarding diagnosis and prognosis in several clinical pathologies. Recently, Dynamic CE-CT (DCE-CT) has emerged as a promising technique to perform also functional hemodynamic studies, with wide applications in the oncologic field. DCE-CT is based on repeated scans over time performed after intravenous administration of contrast agent, in order to study the temporal evolution of the tracer in 3D tumour tissue. DCE-CT pushes towards an intensive use of computers to provide automatically quantitative information to be used directly in clinical practice. This requires that visual analysis, representing the gold-standard for CT image interpretation, gains objectivity.This work presents the first automatic approach to quantify and classify the lung tumour heterogeneities based on DCE-CT image sequences, so as it is performed through visual analysis by experts. The approach developed relies on the spatio-temporal indices we devised, which also allow exploiting temporal data that enrich the knowledge of the tissue heterogeneity by providing information regarding the lesion status.

Keywords: Visual assessment; oncology; image processing; medical imaging; quantitative imaging (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0129183116501060

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