Computational Pipeline in Neuroradiomics.

World Journal of Radiology

A Computational Protocol for Whole Brain Histology Imaging.

A robust, end-to-end pipeline designed to manage and analyze these large-scale volumes efficiently, using a chunk-based data structure (OME-Zarr) to drastically lower hardware memory requirements, enabling the processing of terabyte-scale data on standard workstations.

Recent advances in MR neuroimaging: toward quantitative and AI-driven brain and spinal cord imaging

The pivotal role of deep learning in image reconstruction and acceleration is explored, followed by detailed analyses of quantitative brain oxygen metabolism assessment, standardized spinal cord imaging frameworks, and the non-invasive monitoring of the glymphatic system using diffusion MRI.

ClinSeg: Robust Brain Segmentation for Clinically Acquired Pediatric MRI

Clinical brain MRIs from pediatric health systems represent a viable resource for modeling early neurodevelopmental trajectories and studying neurodevelopmental risk in real-world populations, and a robust segmentation approach tailored to early-life clinical MRIs with variable orientation, resolution, and contrast is...

Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data

This research systematically benchmarks five CNN architectures (VGG19, DenseNet201, ResNet50, Inception-v3, and MobileNet) on balanced and naturally imbalanced MRI datasets, suggesting that VGG19 is particularly good at discriminative performance.

A deep learning framework for automated quantification of peripheral nerve lesions in MR neurography

A reproducible deep learning-based framework for automated segmentation and quantitative assessment of peripheral nerve lesions in MR neurography is introduced and enables standardized volumetric characterization of small intraneural abnormalities and provides a basis for scalable, quantitative peripheral nerve imaging...