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Proceedings Paper

Increasing CAD system efficacy for lung texture analysis using a convolutional network
Author(s): Sebastian Roberto Tarando; Catalin Fetita; Alex Faccinetto; Pierre-Yves Brillet
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Paper Abstract

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. For the large majority of CAD systems, such classification relies on a two-dimensional analysis of axial CT images. In a previously developed CAD system, we proposed a fully-3D approach exploiting a multi-scale morphological analysis which showed good performance in detecting diseased areas, but with a major drawback consisting of sometimes overestimating the pathological areas and mixing different type of lung patterns. This paper proposes a combination of the existing CAD system with the classification outcome provided by a convolutional network, specifically tuned-up, in order to increase the specificity of the classification and the confidence to diagnosis. The advantage of using a deep learning approach is a better regularization of the classification output (because of a deeper insight into a given pathological class over a large series of samples) where the previous system is extra-sensitive due to the multi-scale response on patient-specific, localized patterns. In a preliminary evaluation, the combined approach was tested on a 10 patient database of various lung pathologies, showing a sharp increase of true detections.

Paper Details

Date Published: 24 March 2016
PDF: 10 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850Q (24 March 2016); doi: 10.1117/12.2217752
Show Author Affiliations
Sebastian Roberto Tarando, Télécom SudParis (France)
Catalin Fetita, Télécom SudParis (France)
MAP5 CNRS UMR8145 (France)
Alex Faccinetto, Avicenne Hospital (France)
Pierre-Yves Brillet, Univ. Paris 13 (France)
Avicenne Hospital (France)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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