Brain MR Image
Brain MRI image analysis focuses on extracting meaningful information from brain scans for diagnosis and research. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs) for tasks such as segmentation, registration, anomaly detection, and artifact removal. These advancements aim to improve diagnostic accuracy, automate time-consuming processes like quality assessment and lesion delineation, and facilitate large-scale studies by addressing challenges like data harmonization and imbalanced datasets. Ultimately, improved brain MRI analysis promises to enhance patient care and accelerate scientific discovery in neurology.
Papers
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Pavan Tummala, Shubham Kumar Agrawal, Aishwarya Jauhari, Aman Kalra, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves, Julia Krüger, Roland Opfer, Alexander Schlaefer