The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …

Background White matter hyperintensities (WMH), of presumed vascular origin, are visible
and quantifiable neuroradiological markers of brain parenchymal change. These changes …

Unsupervised domain adaptation has attracted growing research attention on semantic
segmentation. However, 1) most existing models cannot be directly applied into lesions …

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …

The performance of current machine learning methods to detect heterogeneous pathology is
limited by the quantity and quality of pathology in medical images. A possible solution is …

To extract meaningful and reproducible models of brain function from stroke images, for both
clinical and research proposes, is a daunting task severely hindered by the great variability …

Pseudo-healthy synthesis is the task of creating a subject-specific 'healthy'image from a
pathological one. Such images can be helpful in tasks such as anomaly detection and …

White matter hyperintensities (WMH) are imaging manifestations frequently observed in
various neurological disorders, yet the clinical application of WMH quantification is limited. In …

Segmentation methods can mutually exclude the location of the tumor. However, the
challenge of complex location or incomplete identification is located in segmentation …

Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and
T2-weighted magnetic resonance images (MRIs) of the human brain are common in the …