Our review paper on harmonization methods in radiomics, in collaboration with the School for Oncology, Maastricht University, has been published in the Journal of Personalized Medicine.
The paper is now available online: “Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods“, Shruti Atul Mali et al.
Radiomics aims to extract quantitative features from medical images to improve decision support. In this work, we discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. The different harmonization solutions are divided into two main categories: image domain (including image acquisition, post-processing of raw sensor-level image data, data augmentation, and style transfer) and feature domain (including statistical normalization, intensity harmonization, ComBat and deep learning harmonization).