---
license: apache-2.0
tags:
  - medical-imaging
  - image-registration
  - torchscript
  - impact
  - pretrained
  - segmentation
---

# 🧠 TorchScript Models for the IMPACT Semantic Similarity Metric

This repository hosts multiple **TorchScript-exported pretrained models** used by the **IMPACT** similarity metric for semantic medical image registration.

The IMPACT metric is described 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. Downling, S. Rouzé, C. Lafond, A. Barateau, J.-L. Dillenseger*  
> [arXiv:2503.24121 [cs.CV]](https://arxiv.org/abs/2503.24121)

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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  
- **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, which can be selected independently using the corresponding model l_Layers. This can be configured through the LayerMask parameter in the IMPACT configuration.

In addition, the repository also includes:

- **MIND** — A handcrafted Modality Independent Neighborhood Descriptor, wrapped in TorchScript

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## 📚 Pretrained Model References

| Model          | Specialization                        | Paper / Reference                                           | Field of View         | License      |
|----------------|----------------------------------------|-------------------------------------------------------------|------------------------|--------------|
| **MIND**       | Handcrafted descriptor                | [Heinrich et al., 2012](https://doi.org/10.1016/j.media.2012.05.008) | `2r + 1`               | Research only |
| **SAM2.1**     | General segmentation (natural images) | [Ravi et al., 2023](https://arxiv.org/abs/2408.00714)   | 29                    | MIT           |
| **TS Models**  | Multi-resolution CT/MRI segmentation  | [Wasserthal et al., 2022](https://arxiv.org/abs/2208.05868)    | `2^l + 3`             | Apache 2.0    |
| **Anatomix**   | Anatomy-aware transformer encoder     | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)   | Hierarchical          | MIT           |
| **DINOv2**     | Self-supervised vision transformer    | [Oquab et al., 2023](https://arxiv.org/abs/2304.07193)      | Global / ViT-Base     | MIT           |

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### 🔍 TS Model Variants

**TS Models** refer to the following TotalSegmentator-derived TorchScript models:  
`M258, M291, M293, M294, M295, M297, M298, M730, M731, M732, M733, M850, M851`

Each model is specialized for a specific anatomical structure or resolution (e.g., 3mm / 6mm) and shares the same encoder-decoder architecture.  

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