Increasing CAD system efficacy for lung texture analysis using a convolutional network - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Increasing CAD system efficacy for lung texture analysis using a convolutional network

Résumé

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.
Fichier non déposé

Dates et versions

hal-01443107 , version 1 (22-01-2017)

Identifiants

Citer

Sebastian Tarando, Catalin Fetita, Faccinetto Alex, Pierre-Yves Brillet. Increasing CAD system efficacy for lung texture analysis using a convolutional network. SPIE Medical Imaging, Feb 2016, Orlando, United States. pp.97850Q, ⟨10.1117/12.2217752⟩. ⟨hal-01443107⟩
204 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More