Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis
Gianluca Brugnara, Fabian Isensee, Ulf Neuberger, David Bonekamp, Jens Petersen, Ricarda Diem, Brigitte Wildemann, Sabine Heiland, Wolfgang Wick, Martin Bendszus, Klaus Maier‐Hein, Philipp Kickingereder
Topics & Concepts
Fluid-attenuated inversion recoveryMedicineNeuroradiologyConcordanceMagnetic resonance imagingRadiologySørensen–Dice coefficientSegmentationNuclear medicineMultiple sclerosisArtificial intelligenceNeurologyImage segmentationComputer scienceInternal medicinePsychiatryMultiple Sclerosis Research StudiesGlioma Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging