Source code

GitHub organisation

minnelab

The lab's GitHub organisation. Research platforms, publication code, teaching material, and federated-learning infrastructure live here.

Open on GitHub →

Research platforms

  • Medical AI platform

    MAIA

    Kubernetes-based platform for collaborative medical-AI research — data preparation, training, active learning, deployment, and evaluation in one environment.

  • Federated learning

    MONet-Bundle

    MONAI bundle integrating nnU-Net for clinical federated learning, active learning, and PACS integration (MICCAI 2025 DeCaF).

  • Infrastructure

    NVFlare-Dashboard

    NVIDIA FLARE dashboard for provisioning and monitoring federated-learning experiments across institutions.

  • Detection framework

    nnDetection

    Self-configuring framework for 3D medical-object detection. Our fork with lab-specific integrations.

Code accompanying publications

  • Scientific Reports · 2025

    DBM_with_DL

    Decomposing the effect of normal aging and Alzheimer's disease in brain morphological changes via learned aging templates.

  • Medical Image Analysis · 2025

    InBrainSyn

    Synthesising individualised aging brains in health and disease with generative models and parallel transport.

  • ISBI · 2025

    longitudinal-rigid-registration

    Learning accurate rigid registration for longitudinal brain MRI from synthetic data.

  • Tractography

    rCOMMIT

    Randomly COMMITting — iterative convex optimisation for microstructure-informed tractography.

  • ADSMI @ MICCAI · 2024

    DA_nnUNet

    Unsupervised domain adaptation for pediatric brain-tumor segmentation.

  • HBM · 2023

    Synthetic-Brain-Aging

    Fast 3D image generation for healthy brain aging using diffeomorphic registration.

  • Breast ultrasound

    DynamicBUS

    Diffusion-model-based breast ultrasound video generation for diagnosis.

Teaching & workshops

For a full list — including internal tools and configuration repositories — see the minnelab organisation on GitHub.

Rodrigo Moreno — personal repositories

GitHub user

rodrigomorenokth

Earlier reference implementations published directly under Rodrigo Moreno's personal GitHub account.

Open on GitHub →
  • Vascular imaging

    RPD

    Ring Pattern Detector — reference implementation of the vesselness filter for tubular-structure extraction.

  • Diffusion MRI

    GMIL

    Generalised Mean Intercept Length Tensor — anisotropy estimation for orientation-distribution data.

Datasets

Open dataset

Synthetic Healthy Brain Aging MRIs with Segmentation Masks

A collection of 6,448 synthetic T1-weighted MRI scans simulating longitudinal brain aging, each with a corresponding segmentation mask. Generated using deep generative models to support research in neuroimaging and healthy aging.

Modality
T1-weighted MRI (synthetic)
Resolution
160 × 160 × 192 voxels, 1 × 1 × 1 mm
Volume
6,448 scans, 52.4 GB
Citation

Jingru Fu, Antonios Tzortzakakis, José Barroso, Eric Westman, Daniel Ferreira, Rodrigo Moreno (2023). Synthetic healthy brain aging MRIs with segmentation masks. doi:10.23698/aida/synthetic/shbamri