MIR: Medical Image Registration toolkit

MIR is a PyTorch research toolkit for deformable medical image registration. It brings state-of-the-art registration networks (TransMorph, VFA, SITReg, VoxelMorph, LessNet, Fourier-Net and their variants), classical baselines, training losses, evaluation metrics and reproducible benchmark pipelines together behind one interface: a (moving, fixed) pair in, a dense displacement field out.

Documentation: https://junyuchen.me/MIR

Highlights

• One convention for every model. A registration network takes a (moving, fixed) pair of [B, 1, D, H, W] tensors and returns a displacement field in voxels that SpatialTransformer applies to any image or label map. Swapping architectures is a one-line change.
• Diffeomorphic by design. Choose the transformation model: displacement, stationary or time-varying velocity fields, symmetric inverse-consistent mappings, or LDDMM geodesic shooting.
• Spatially varying regularization (SPR): learn where the deformation should be smooth and where it may be sharp, with a per-voxel regularization map and a Beta prior.
• Hypernetwork conditioning (HyperMorph, HyperTransMorph, HyperVFA) to cover a range of regularization strengths with one model.
• Mixed precision and multi-GPU. Train and run any model in float16 or bfloat16 and on several GPUs with DistributedDataParallel or DataParallel; losses and warping stay in float32 for accuracy (docs, example).
• Runs in the browser. Models export to ONNX; the registration playground runs a 2D HyperVFA-SPR with ONNX Runtime Web (tutorial, docs).
• Classical and third-party baselines behind the same interface: ConvexAdam, deedsBCV, FireANTs, SITReg, MultiMorph, intensity normalization.
• Training and evaluation batteries: NCC / SSIM / MIND / mutual information / correlation ratio / Dice losses, diffusion and local regularizers, Beta priors, GradICON, Jacobian-based folding metrics, augmentation, and a registry of pretrained weights.

Installation

Install a CUDA-enabled PyTorch build first, then MIR in editable mode:

git clone https://github.com/junyuchen245/MIR.git
cd MIR
python3.8 -m pip install -U pip
python3.8 -m pip install -e .

Python 3.8+ is required. deedsBCV is Linux-only; everything else is platform independent. See the installation guide for optional dependencies and the pretrained weights.

Sixty-second example

import torch, nibabel as nib
from MIR import ModelWeights
from MIR.models import VFA, SpatialTransformer
import MIR.models.configs_VFA as configs_VFA

mov = torch.from_numpy(nib.load('subject.nii.gz').get_fdata()[None, None]).float().cuda()
fix = torch.from_numpy(nib.load('template.nii.gz').get_fdata()[None, None]).float().cuda()

config = configs_VFA.get_VFA_default_config()
config.img_size = fix.shape[2:]
model = VFA(config, device='cuda:0').cuda().eval()
model.load_state_dict(torch.load('VFA_LUMIR24.pth')[ModelWeights['VFA-LUMIR24-MonoModal']['wts_key']])

with torch.no_grad():
    flow = model((mov, fix))                       # [1, 3, D, H, W] displacement in voxels
warped = SpatialTransformer(fix.shape[2:]).cuda()(mov, flow)

Models

Family	Summary	Docs
TransMorph, TransMorph-TVF, TransMorph-TVF-SPR, TransMorph-DCA	Swin-Transformer registration networks	TransMorph
VFA, VFA-SPR, TransVFA, HyperVFA	Vector field attention	VFA
HyperTransMorph, HyperMorph	Hypernetwork-conditioned regularization	HyperTransMorph
VoxelMorph, LessNet, Fourier-Net(+)	Convolutional baselines (with SVF and SPR variants)	VoxelMorph, LessNet, Fourier-Net
SITReg	Symmetric, inverse-consistent, topology-preserving registration	SITReg
ConvexAdam, FireANTs, deedsBCV	Optimization-based and classical baselines	ConvexAdam, deedsBCV
TransMorph-LDDMM, LDDMMWrapper	Time-varying velocity fields (geodesic shooting) for any backbone	LDDMM
MultiMorph, TemplateCreation	Atlas construction	Atlas construction

Conventions, transformation models, regularization, losses, metrics and a complete training recipe are explained in the user guide.

Tutorials

Runnable training, inference and evaluation scripts live under tutorials/, one folder per dataset and model (see the tutorial catalogue and tutorials/README.md). Good starting points:

• tutorials/brain_registration: affine + deformable brain registration with pretrained weights.
• tutorials/IXI_benchmarking: benchmark seven models on IXI from a notebook.
• tutorials/lumir25_TransMorph: a complete training template (Learn2Reg LUMIR25).
• tutorials/build_brain_template: population template construction.

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗