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IMPACT-Reg-KonfAI

Fast and lightweight CLI for multimodal medical image registration using IMPACT-Reg presets within the KonfAI framework.


🧩 Overview

IMPACT-Reg-KonfAI is the command-line interface (CLI) for running IMPACT-Reg registration presets published in the VBoussot/ImpactReg Hugging Face repository, through the KonfAI deep learning framework.

IMPACT-Reg introduces a semantic similarity metric for multimodal registration, driven by deep features extracted from large pretrained segmentation and foundation models (MIND, TotalSegmentator, MRSegmentator). It plugs into an Elastix-based multi-resolution deformable pipeline to achieve robust cross-modality alignment while keeping deformations smooth and physically plausible.

A registration run combines:

  • fixed and moving images
  • one or more registration presets resolved from the published preset database (each preset is a KonfAI app)
  • optional image, segmentation, or landmark references (with an optional mask) for evaluation

🧠 Features

  • Fast registration powered by KonfAI
  • 🤗 Automatic preset, parameter-map, and model download from Hugging Face
  • 🧩 Multi-preset ensembling (transforms averaged into a single displacement field)
  • 🧠 Semantic IMPACT metric on deep features from pretrained segmentation / foundation models
  • 📐 Evaluation workflows against image, segmentation, and landmark references
  • 🧾 Multi-format compatibility: supports all major medical image formats handled by ITK

🗂️ Available presets

Presets are resolved dynamically from the published preset database (PresetDatabase.json) and passed as the first positional argument(s). Current presets include generic rigid / rigid + BSpline strategies and IMPACT-driven deformable presets tuned per modality pair (MR/CT, CBCT/CT) and anatomy (generic, head & neck).

List the presets exposed by your installation with:

impact-reg-konfai register --help

🚀 Installation

From PyPI:

python -m pip install impact-reg-konfai

From source:

git clone https://github.com/fideus-labs/KonfAI.git
cd KonfAI
# konfai and konfai-apps must come from the same checkout: this app pins both to its own
# setuptools_scm version, which only exists on PyPI at a release tag.
python -m pip install -e . -e konfai-apps -e apps/impact_reg

⚙️ Usage

The CLI is organised into sub-commands, matching the registration workflow:

Sub-command Purpose
register Register a moving image onto a fixed image with one or more presets. Several presets are ensembled (their displacement fields are averaged). Writes the transform under the name and in the form the preset declared, the moved image derived from it, and, with --uncertainty, the per-preset fields (kept under Ensemble/ for uncertainty).
eval Evaluate a registration on any subset of modalities: image (MAE), segmentation (Dice), landmarks (TRE). At least one modality is required.
uncertainty Voxel-wise spread map from an ensemble of displacement fields.

Register a moving image onto a fixed image (ensemble several presets by listing them):

impact-reg-konfai register <PRESET> [<PRESET_2> ...] -f fixed.nii.gz -m moving.nii.gz -o ./Output --gpu 0

Evaluate a registration on any subset of modalities; the transform comes from a prior register:

impact-reg-konfai eval \
  --transform ./Output/P000/DVF.mha \
  -f fixed.nii.gz -m moving.nii.gz --mask roi.nii.gz \
  --gt-fixed-seg fixed_seg.nii.gz --gt-moving-seg moving_seg.nii.gz \
  --gt-fixed-fid fixed.fcsv --gt-moving-fid moving.fcsv \
  -o ./Output --gpu 0

Estimate uncertainty from the per-preset displacement fields written by register:

impact-reg-konfai uncertainty --dvf ./Output/P000/Ensemble/*.mha -o ./Output/P000

register arguments

Flag Description Default
PRESET One or more presets from the published preset database (several are ensembled) required
-f, --fixed-images Fixed image(s), or a dataset directory required
-m, --moving-images Moving image(s), or a dataset directory required
--fixed-mask, --moving-mask Optional masks restricting the metric region unset
-o, --output Output directory ./Output/
--tta Test-time-augmentation draws per preset 0
--uncertainty Keep the per-preset fields under Ensemble/ for a later uncertainty run False
--fields-only Write the transforms and stop; skip deriving the moved images False
--set NAME=VALUE Override a preset parameter (repeatable, forwarded to konfai-apps infer) unset
--tmp-dir Where the intermediates are staged system temp
--gpu / --cpu GPU id(s) / CPU worker processes CPU if unset
-q, --quiet Suppress console output False

eval arguments (at least one modality required)

Flag Description Default
--transform Transform(s) from a prior register (identity if omitted) unset
-f, -m Fixed / moving images: image modality (MAE) unset
--gt-fixed-seg, --gt-moving-seg Fixed / moving segmentations: seg modality (Dice) unset
--gt-fixed-fid, --gt-moving-fid Fixed / moving landmarks: fid modality (TRE) unset
--mask Evaluation mask(s) for the image modality unset
--preset Preset providing the evaluation configs first available

uncertainty arguments

Flag Description Default
--dvf Two or more ensemble displacement fields (e.g. the per-preset fields from register) required
-o, --output Output directory ./Output/

See the full help of any sub-command with:

impact-reg-konfai register --help

📦 Notes

  • Available presets are resolved dynamically from the published IMPACT-Reg preset database.
  • Multiple presets can be provided in one command; their displacement fields are averaged into a single field.
  • The wrapper orchestrates the preset KonfAI apps (model inference), then ensembles, evaluates, and estimates uncertainty on their outputs.

📚 References

If you use IMPACT-Reg-KonfAI in your work, please cite KonfAI and the IMPACT-Reg paper.

  • Boussot, V., Hémon, C., Nunes, J.-C., Dowling, J., Rouzé, S., Lafond, C., Barateau, A., & Dillenseger, J.-L. IMPACT-Reg: A Generic Semantic Loss for Multimodal Medical Image Registration.

  • Boussot, V., & Dillenseger, J.-L. (2025). KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging. arXiv preprint arXiv:2508.09823


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