File size: 4,043 Bytes
48f6963 e00ba49 48f6963 e00ba49 48f6963 806be59 48f6963 e00ba49 48f6963 e00ba49 48f6963 f315bc9 48f6963 f315bc9 48f6963 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
license: apache-2.0
tags:
- medical-imaging
- image-registration
- torchscript
- impact
- pretrained
- segmentation
---
# 🧠 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]](https://arxiv.org/abs/2503.24121)
🔧 The full implementation of IMPACT, along with its integration into the **Elastix** framework, is available in the repository:
➡️ [github.com/vboussot/ImpactLoss](https://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.
---
## 📚 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
- **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 descriptor, wrapped in TorchScript
| Model | Specialization | Paper / Reference | Field of View | License | Preprocessing |
|----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------|
| **MIND** | Handcrafted descriptor | [Heinrich et al., 2012](https://doi.org/10.1016/j.media.2012.05.008) | `2*r*d + 1` (r: radius, d: dilation) | Apache 2.0 | None |
| **SAM2.1** | General segmentation (natural images) | [Ravi et al., 2023](https://arxiv.org/abs/2408.00714) | 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](https://arxiv.org/abs/2208.05868) | `2^l + 3` (l: layer number) | Apache 2.0 | Canonical orientation for all models. For MRI models (e.g., TS/M730–M733), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities to [-1024, 276] HU, then normalize by centering at -370 HU and scaling by 436.6.|
| **Anatomix** | Anatomy-aware transformer encoder | [Dey et al., 2024](https://arxiv.org/abs/2411.02372) | Global(Static mode) | MIT | Normalize intensities to [0, 1] |
| **DINOv2** | Self-supervised vision transformer | [Oquab et al., 2023](https://arxiv.org/abs/2304.07193) | 14 | Apache 2.0 | Normalize intensities to [0, 1], then standardize with mean 0.485 and std 0.229 |
---
### 🔍 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.
--- |