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, CT (Computer Tomography) scanner producers, pixel spacing, acquisition protocol and reconstruction parameters. This paper introduces a new method to transform image features in order to improve their stability across scanner producers and scanner models. This method is based on a two-layer neural network that can learn a non-linear standardization transformation of various types of features including hand-crafted and deep features. A publicly available database of phantom images with ground truth is used where the same physical phantom was scanned on 17 different CT scanners. In this setting, variations in extracted features are representative of true physio-pathological tissue changes in the scanned patients, so harmonized between scanner producers and models. The recent success of radiomics studies has often been restricted to relatively controlled environments. In order allow for comparing data of several hospitals produced with a larger variety of scanner producers and models as well as with several protocols, features standardization seems necessary to keep results comparable.
Vincent Andrearczyk, Adrien Depeursinge, and Henning Müller, "Learning cross-protocol radiomics and deep feature standardization from CT images of texture phantoms," Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540I (Presented at SPIE Medical Imaging: February 17, 2019; Published: 15 March 2019); https://doi.org/10.1117/12.2512683.
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