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impact-torchscript-models

medical-imaging
image-registration
torchscript
impact
pretrained
segmentation
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Valentin Boussot commited on
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Initial upload of TorchScript models for IMPACT

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+ ---
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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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+ ---
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+
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+ # 🧠 TorchScript Models for the IMPACT Semantic Similarity Metric
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+
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+ This repository hosts multiple **TorchScript-exported pretrained models** used by the **IMPACT** similarity metric for semantic medical image registration.
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+
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+ The IMPACT metric is described in the following preprint, currently under review:
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+
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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. Downling, 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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+
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+ ---
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+
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+ The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include:
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+
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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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+
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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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+
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+ In addition, the repository also includes:
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+
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+ - **MIND** — A handcrafted Modality Independent Neighborhood Descriptor, wrapped in TorchScript
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+
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+ ---
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+
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+ ## 📚 Pretrained Model References
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+
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+ | Model | Specialization | Paper / Reference | Field of View | License |
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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) | `2r + 1` | Research only |
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+ | **SAM2.1** | General segmentation (natural images) | [Ravi et al., 2023](https://arxiv.org/abs/2408.00714) | 29 | MIT |
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+ | **TS Models** | Multi-resolution CT/MRI segmentation | [Wasserthal et al., 2022](https://arxiv.org/abs/2208.05868) | `2^l + 3` | Apache 2.0 |
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+ | **Anatomix** | Anatomy-aware transformer encoder | [Dey et al., 2024](https://arxiv.org/abs/2411.02372) | Hierarchical | MIT |
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+ | **DINOv2** | Self-supervised vision transformer | [Oquab et al., 2023](https://arxiv.org/abs/2304.07193) | Global / ViT-Base | MIT |
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+
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+ ---
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+
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+ ### 🔍 TS Model Variants
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+
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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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+
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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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+
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+ ---
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