A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence in Lung CT
We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry requiring specific geodesic metrics to locally approximate scalar products. The latter are used to construct a kernel for support vector machines (SVM). The effectiveness of the presented models is evaluated on a dataset of 92 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy above 80, and highlighted the importance of covariance-based texture aggregation. At the end of the talk, computer tools will be presented to easily extract 3D radiomics quantitative features from PET-CT images.
Pol Cirujeda, Yashin Dicente Cid, Henning Müller, Daniel Rubin, Todd A. Aguilera, Billy W. Loo Jr., Maximilian Diehn, Xavier Binefa, Adrien Depeursinge
Nom du congrès, lieu, date
QTIM Special guest lecture, Athinoula A. Martinos Center for Biomedical Imaging, du 27.07.2016 au 27.07.2016