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eigenvivek
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xvr

Image-to-Image
PyTorch
medical-imaging
registration
3d-registration
x-ray
ct
mri
Model card Files
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Upload final models

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  1. .gitattributes +35 -0
  2. README.md +45 -0
  3. deepfluoro/de_novo/subject01.pth +3 -0
  4. deepfluoro/de_novo/subject02.pth +3 -0
  5. deepfluoro/de_novo/subject03.pth +3 -0
  6. deepfluoro/de_novo/subject04.pth +3 -0
  7. deepfluoro/de_novo/subject05.pth +3 -0
  8. deepfluoro/de_novo/subject06.pth +3 -0
  9. deepfluoro/finetuned/subject01.pth +3 -0
  10. deepfluoro/finetuned/subject02.pth +3 -0
  11. deepfluoro/finetuned/subject03.pth +3 -0
  12. deepfluoro/finetuned/subject04.pth +3 -0
  13. deepfluoro/finetuned/subject05.pth +3 -0
  14. deepfluoro/finetuned/subject06.pth +3 -0
  15. femur/de_novo/subject01.pth +3 -0
  16. femur/de_novo/subject02.pth +3 -0
  17. femur/de_novo/subject03.pth +3 -0
  18. femur/de_novo/subject04.pth +3 -0
  19. femur/de_novo/subject05.pth +3 -0
  20. femur/finetuned/subject01.pth +3 -0
  21. femur/finetuned/subject02.pth +3 -0
  22. femur/finetuned/subject03.pth +3 -0
  23. femur/finetuned/subject04.pth +3 -0
  24. femur/finetuned/subject05.pth +3 -0
  25. ljubljana/de_novo/subject01.pth +3 -0
  26. ljubljana/de_novo/subject02.pth +3 -0
  27. ljubljana/de_novo/subject03.pth +3 -0
  28. ljubljana/de_novo/subject04.pth +3 -0
  29. ljubljana/de_novo/subject05.pth +3 -0
  30. ljubljana/de_novo/subject06.pth +3 -0
  31. ljubljana/de_novo/subject07.pth +3 -0
  32. ljubljana/de_novo/subject08.pth +3 -0
  33. ljubljana/de_novo/subject09.pth +3 -0
  34. ljubljana/de_novo/subject10.pth +3 -0
  35. ljubljana/finetuned/subject01.pth +3 -0
  36. ljubljana/finetuned/subject02.pth +3 -0
  37. ljubljana/finetuned/subject03.pth +3 -0
  38. ljubljana/finetuned/subject04.pth +3 -0
  39. ljubljana/finetuned/subject05.pth +3 -0
  40. ljubljana/finetuned/subject06.pth +3 -0
  41. ljubljana/finetuned/subject07.pth +3 -0
  42. ljubljana/finetuned/subject08.pth +3 -0
  43. ljubljana/finetuned/subject09.pth +3 -0
  44. ljubljana/finetuned/subject10.pth +3 -0
  45. wbct/model.pth +3 -0
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README.md ADDED
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+ ---
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+ license: mit
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+ library_name: pytorch
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+ pipeline_tag: image-to-image
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+ tags:
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+ - medical-imaging
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+ - registration
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+ - 3d-registration
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+ - x-ray
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+ - ct
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+ - mri
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+ ---
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+
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+ # `xvr`: X-ray to Volume Registration
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+
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+ [![Paper shield](https://img.shields.io/badge/arXiv-2503.16309-red.svg)](https://arxiv.org/abs/2503.16309)
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+ [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
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+ <a href="https://colab.research.google.com/drive/1K9lBPxcLh55mr8o50Y7aHkjzjEWKPCrM?usp=sharing"><img alt="Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a>
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+ <a href="https://huggingface.co/eigenvivek/xvr/tree/main" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-ffc107?color=ffc107&logoColor=white"/></a>
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+ <a href="https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Data-ffc107?color=ffc107&logoColor=white"/></a>
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+ [![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
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+
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+ **`xvr` is a PyTorch package for training, fine-tuning, and performing 2D/3D X-ray to CT/MR registration using pose regression models.** It provides a streamlined CLI and API for training patient-specific registration models efficiently. Key features include significantly faster training than comparable methods, submillimeter registration accuracy, and human-interpretable pose parameters.
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+
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+
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+ <p align="center">
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+ <img width="410" alt="image" src="https://github.com/user-attachments/assets/8a01c184-f6f1-420e-82b9-1cbe733adf7f" />
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+ </p>
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+
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+ ## Key Features
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+
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+ - 🚀 Single CLI/API for training and registration.
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+ - ⚡️ Significantly faster training than existing methods.
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+ - 📐 Submillimeter registration accuracy.
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+ - 🩺 Human-interpretable pose parameters.
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+ - 🐍 Pure Python/PyTorch implementation.
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+ - 🖥️ Cross-platform support (macOS, Linux, Windows).
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+
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+ `xvr` leverages [`DiffDRR`](https://github.com/eigenvivek/DiffDRR), the differentiable X-ray renderer.
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+
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+ ## Installation and Usage
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+
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+ This repository contains pretrained pose regression models to be run on public benchmarks datasets (reformatted into DICOM/NIfTI format) available [here](https://huggingface.co/datasets/eigenvivek/xvr-data/tree/main).
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+
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+ It also contains a foundation pose regression model (`wbct/model.pth`) intended to be quickly finetuned on new scans. Please refer to the [GitHub repository](https://github.com/eigenvivek/xvr) for detailed installation instructions, usage examples, and documentation on training, finetuning, and registration.
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