TL;DR
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.
Background: Whole-brain segmentation of neonatal low-field MRI is needed for regional volumetric analysis, but established segmentation systems are designed around different field strengths, contrasts, and age ranges. Methods: We first applied complete end-to-end pipelines, including FreeSurfer, FSL-based processing, I...
This chapter presents a step-by-step implementation of the neuroradiomics pipeline, with recommended tools and illustrative Python scripts and command-line snippets provided to support both research and clinical applications.
A unified framework for the LISA 2026 Challenge that performs all three tasks together within one inference pipeline, and addresses the scarcity of dense annotations using diffeomorphic registration from atlas to target for label propagation and to regularize anatomical reconstruction.
3D-MAESTRO is applied to lightsheet images of whole mouse brains in the context of diverse anatomical and functional experiments, illustrating high-throughput and reproducible mapping of microscopic structures across the brain.
Multisite functional Magnetic Resonance Imaging (fMRI) studies are rapidly becoming the norm to allow the acquisition of large samples required to perform advanced statistical techniques, e.g. machine learning. However, acquiring data at multiple sites includes methodological and technical challenges due to the differe...
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