Digitally reconstructed radiographs (DRRs) are synthetic projections derived from computed tomography (CT) data and are increasingly used in 3D synthetic image generation. However, variability in preprocessing and projection frameworks limits reproducibility and comparability across studies. We present a standardized p...
Massimo Bottini, Istiak Khan, Olivier Zanier et al.· Scientific Reports· 0 citations
Segmentations of the vertebral column that include anatomical subregions can be used for patient education, pedicle screw planning, or radiomic feature extraction for spinal surgery. Deep learning has proven successful in tackling medical image segmentation; therefore, we aim to train a multiclass vertebral subregion s...
Raffaele Da Mutten, Sven Theiler, Massimo Bottini et al.· Journal of imaging informati...· 0 citations
BACKGROUND
Advances in generative artificial intelligence (AI) have accelerated the development and application of synthetic medical imaging. Despite this rapid progress, the evaluation of synthetic medical images remains heterogeneous, with numerous metrics proposed to assess fidelity, realism, diversity, and clinical...
D. D. de Wilde, Benjamin Schärli, Kym Ackermann et al.· European Journal of Radiolog...· 0 citations
INTRODUCTION
Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based mo...
D. de Wilde, Olivier Zanier, A. Alakmeh et al.· Neuroradiology· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
