Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …

Background and ObjectiveProcessing of medical images such as MRI or CT presents
different challenges compared to RGB images typically used in computer vision. These …

Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …

Background Portable, low-field-strength (0.064-T) MRI has the potential to transform
neuroimaging but is limited by low spatial resolution and low signal-to-noise ratio. Purpose …

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are
designed for data with near-isotropic voxels at approximately 1 mm resolution, and …

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm
for improved structure definition and morphometry. Yet, only few, time-intensive automated …

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord
cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or …

The robust delineation of the cochlea and its inner structures combined with the detection of
the electrode of a cochlear implant within these structures is essential for envisaging a safer …