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A unified computational framework for performing cortical reconstruction and anatomical parcellation on high-resolution postmortem infant MRI. It combines deep learning segmentation with a specialized synthetic data generation engine (PostSynth) to handle postmortem domain shifts.

CortPro is an open-source toolbox providing a standardized workflow for intracortical microstructural profiling, analyzing tissue properties across cortical depths using surface-based sampling.

MRIQC is a software tool for performing quality assessment and control (QC) on unprocessed anatomical, functional, and diffusion MRI scans of the human brain. It generates visual reports to help researchers identify artifacts and quality issues prior to detailed analysis.

BIBSNet is an open-source deep learning model designed for robust and generalizable brain tissue segmentation in MRI scans of infants (0-8 months). It processes MR data to segment various brain tissues, which is critical for studying early brain development.

YODA (You Only Denoise once - or Average) is a 2.5D diffusion-based medical image translation framework that averages multiple diffusion samples (Expectation-Approximation sampling) to generate accurate, hallucination-free cross-modality MRI/CT images, distributed as a containerized command-line tool with test data on Zenodo.