Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in
multicentre settings is an important criterion for clinical translation. We therefore performed a …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

Precise image segmentation provides clinical study with instructive information. Despite the
remarkable progress achieved in medical image segmentation, there is still an absence of a …

The domain of investigation of radiomics consists of large-scale radiological image analysis
and association with biological or clinical endpoints. The purpose of the present study is to …

Accurate quantification in PET and SPECT requires correction for a number of physical
factors, such as photon attenuation, Compton scattering and random coincidences (in PET) …

Texture indices are of growing interest for tumor characterization in 18F-FDG PET. Yet, on
the basis of results published in the literature so far, it is unclear which indices should be …

Radiomics, the high-throughput extraction of quantitative imaging features from medical
images, holds immense potential for advancing precision medicine in oncology and beyond …

Since its introduction in the early nineties as a promising functional imaging technique in the
management of neoplastic disorders, FDG-PET, and subsequently FDG-PET/CT, has …

Evaluation of tumor heterogeneity based on texture parameters has recently attracted much
interest in the PET imaging community. However, the impact of reconstruction settings on …