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license: apache-2.0 |
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tags: |
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- medical-imaging |
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- image-registration |
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- torchscript |
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- impact |
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- pretrained |
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- segmentation |
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# 🧠 TorchScript Models for the IMPACT Semantic Similarity Metric |
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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. |
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The IMPACT metric is introduced in the following preprint, currently under review: |
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> **IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration** |
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> *V. Boussot, C. Hémon, J.-C. Nunes, J. Dowling, S. Rouzé, C. Lafond, A. Barateau, J.-L. Dillenseger* |
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> [arXiv:2503.24121 [cs.CV]](https://arxiv.org/abs/2503.24121) |
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🔧 The full implementation of IMPACT, along with its integration into the **Elastix** framework, is available in the repository: |
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➡️ [github.com/vboussot/ImpactLoss](https://github.com/vboussot/ImpactLoss) |
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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 |
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The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include: |
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- **TotalSegmentator (TS)** — U-Net models trained for full-body anatomical segmentation |
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- **Segment Anything 2.1 (SAM2.1)** — Foundation model for segmentation on natural images |
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- **DINOv2** — Self-supervised vision transformer trained on diverse datasets |
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- **Anatomix** — Transformer-based model with anatomical priors for medical images |
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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. |
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In addition, the repository also includes: |
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- **MIND** — A handcrafted descriptor, wrapped in TorchScript |
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| Model | Specialization | Paper / Reference | Field of View | License | Preprocessing | |
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|----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------| |
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| **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 | |
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| **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 | |
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| **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.| |
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| **Anatomix** | Anatomy-aware transformer encoder | [Dey et al., 2024](https://arxiv.org/abs/2411.02372) | Global(Static mode) | MIT | Normalize intensities to [0, 1] | |
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| **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 | |
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### 🔍 TS Model Variants |
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**TS Models** refer to the following TotalSegmentator-derived TorchScript models: |
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`M258, M291, M293, M294, M295, M297, M298, M730, M731, M732, M733, M850, M851` |
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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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