Biomedical Imaging GroupSTI
English only   BIG > Publications > Radiomic Standardization

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Neural Network Training for Cross-Protocol Radiomic Feature Standardization in Computed Tomography

V. Andrearczyk, A. Depeursinge, H. Müller

Journal of Medical Imaging, vol. 6, no. 2, pp. 024008-1/024008-13, April-June 2019.

Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, and reconstruction parameters. We propose and compare two methods to transform quantitative image features in order to improve their stability across varying image acquisition parameters while preserving the texture discrimination abilities. In this way, variations in extracted features are representative of true physiopathological tissue changes in the scanned patients. A first approach is based on a two-layer neural network that can learn a nonlinear standardization transformation of various types of features including handcrafted and deep features. Second, domain adversarial training is explored to increase the invariance of the transformed features to the scanner of origin. The generalization of the proposed approach to unseen textures and unseen scanners is demonstrated by a set of experiments using a publicly available computed tomography texture phantom dataset scanned with various imaging devices and parameters.

AUTHOR="Andrearczyk, V. and Depeursinge, A. and M{\"{u}}ller, H.",
TITLE="Neural Network Training for Cross-Protocol Radiomic Feature
        Standardization in Computed Tomography",
JOURNAL="Journal of Medical Imaging",

© 2019 SPIE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from SPIE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.