VBoussot/impact-torchscript-models

🧠 TorchScript Models for the IMPACT Semantic Similarity Metric

This repository provides a collection of TorchScript-exported pretrained models designed for use with the IMPACT similarity metric, enabling semantic medical image registration through feature-level comparison.

The IMPACT metric is introduced in the following preprint, currently under review:

IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
V. Boussot, C. Hémon, J.-C. Nunes, J. Dowling, S. Rouzé, C. Lafond, A. Barateau, J.-L. Dillenseger
arXiv:2503.24121 [cs.CV]

🔧 The full implementation of IMPACT, along with its integration into the Elastix framework, is available in the repository:
➡️ github.com/vboussot/ImpactLoss

This repository also includes example parameter maps, TorchScript model handling utilities, and a ready-to-use Docker environment for quick experimentation and reproducibility.

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📚 Pretrained Model

The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include:

• TotalSegmentator (TS) — U-Net models trained for full-body anatomical segmentation
• MRSegmentator (MRSeg) — U-Net models trained for full-body anatomical segmentation in MRI and CT
• Segment Anything 2.1 (SAM2.1) — Foundation model for segmentation on natural images
• DINOv2 — Self-supervised vision transformer trained on diverse datasets
• Anatomix — Transformer-based model with anatomical priors for medical images

Each model provides multiple feature extraction layers. This can be configured through the LayerMask parameter in the IMPACT configuration.

In addition, the repository also includes:

• MIND — A handcrafted descriptor, wrapped in TorchScript

Model	Specialization	Paper / Reference	Field of View	License	Preprocessing
MIND	Handcrafted descriptor	Heinrich et al., 2012	2*r*d + 1 (r: radius, d: dilation)	Apache 2.0	Normalize intensities to [0, 1]
SAM2.1	General segmentation (natural images)	Ravi et al., 2023	29	Apache 2.0	Normalize intensities to [0, 1], then standardize with mean 0.485 and std 0.229
TS Models	CT/MRI segmentation	Wasserthal et al., 2022	2^l + 3 (l: layer number)	Apache 2.0	Canonical orientation for all models. For MRI models (e.g., TS/M730–M733-M850–M853), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities + normalize model dependant
MRSegmentator	CT/MRI segmentation	Häntze et al., 2024	2^l + 3 (l: layer number)	Apache 2.0	Standardize intensities to zero mean and unit variance.
Anatomix	Anatomy-aware transformer encoder	Dey et al., 2024	Global(Static mode)	MIT	Normalize intensities to [0, 1]
DINOv2	Self-supervised vision transformer	Oquab et al., 2023	14	Apache 2.0	Normalize intensities to [0, 1], then standardize with mean 0.485 and std 0.229

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