Prof. Adrien Depeursinge was a special guest at the QTIM meeting in Boston last July 27, 2016. The topic of his talk was ‘A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence in Lung CT’\u00a0<\/p>\n
Abstract:
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.<\/p>\n
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Prof. Adrien Depeursinge was a special guest at the QTIM meeting in Boston last July 27, 2016. The topic of his talk was ‘A 3-D Riesz-Covariance Texture Model for the Prediction of Nodule Recurrence in Lung CT’\u00a0 Abstract:We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional … <\/p>\n