On an MRI scan of the brain, the boundary between endocrine tissues is highly convoluted
and irregular. Outdated segmentation algorithms face a severe test. Machine learning as a …

Central nervous system (CNS) tumors come with vastly heterogeneous histologic,
molecular, and radiographic landscapes, rendering their precise characterization …

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing
pathologies is challenging due to tissue appearance changes, and still an unsolved …

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance
images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated …

Registration of pre-operative and post-recurrence brain images is often needed to evaluate
the effectiveness of brain gliomas treatment. While recent deep learning-based deformable …

In this paper, we propose a new method for simultaneously segmenting brain scans of
glioma patients and registering these scans to a normal atlas. Performing joint segmentation …

Registration of images in the presence of intra-image signal fluctuations is a challenging
task. The definition of an appropriate objective function measuring the similarity between the …

Medical image registration is a critical task that estimates the spatial correspondence
between pairs of images. However, current traditional and learning-based methods rely on …

Tumor growth models have the potential to model and predict the spatiotemporal evolution
of glioma in individual patients. Infiltration of glioma cells is known to be faster along the …

Purpose To test the hypothesis that combined features from MR and digital histopathologic
images more accurately predict overall survival (OS) in patients with glioma compared with …