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impact-torchscript-models

medical-imaging
image-registration
torchscript
impact
pretrained
segmentation
Model card Files
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VBoussot commited on
Commit
0d472a8
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1 Parent(s): dbda56b

Move input normalization to models and make layer selection part of forward()

Browse files

The C++ wrapper inspects the TorchScript forward() schema and builds the
argument list accordingly, ensuring backward compatibility with legacy models
(forward(input) or forward(input, layers)) while supporting the new extended
signature:

forward(input, layersCount, imageStats, imageDirection)

This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. Anatomix/Anatomix.pt +2 -2
  2. DinoV2/Small_1_Layers.pt → Dino/DinoV2_Small.pt +2 -2
  3. TS/M291_3_Layers.pt → MIND/R1D1_2D.pt +2 -2
  4. MIND/{R2D2.pt → R1D1_3D.pt} +2 -2
  5. MIND/{R1D1.pt → R1D2_2D.pt} +2 -2
  6. MIND/{R1D2.pt → R1D2_3D.pt} +2 -2
  7. MIND/{R2D1.pt → R2D1_2D.pt} +2 -2
  8. MIND/R2D1_3D.pt +3 -0
  9. MIND/R2D2_2D.pt +3 -0
  10. MIND/R2D2_3D.pt +3 -0
  11. MRSeg/MRSeg.pt +3 -0
  12. MRSeg/MRSeg_1_Layers.pt +0 -3
  13. MRSeg/MRSeg_2_Layers.pt +0 -3
  14. MRSeg/MRSeg_3_Layers.pt +0 -3
  15. MRSeg/MRSeg_4_Layers.pt +0 -3
  16. MRSeg/MRSeg_5_Layers.pt +0 -3
  17. MRSeg/MRSeg_6_Layers.pt +0 -3
  18. MRSeg/MRSeg_7_Layers.pt +0 -3
  19. MRSeg/MRSeg_8_Layers.pt +0 -3
  20. README.md +5 -13
  21. SAM2.1/SAM2.1_Small.pt +3 -0
  22. SAM2.1/SAM2.1_Tiny.pt +3 -0
  23. SAM2.1/Small_1_Layers.pt +0 -3
  24. SAM2.1/Small_2_Layers.pt +0 -3
  25. SAM2.1/Small_3_Layers.pt +0 -3
  26. SAM2.1/Small_4_Layers.pt +0 -3
  27. SAM2.1/Small_5_Layers.pt +0 -3
  28. SAM2.1/Tiny_1_Layers.pt +0 -3
  29. SAM2.1/Tiny_2_Layers.pt +0 -3
  30. SAM2.1/Tiny_3_Layers.pt +0 -3
  31. SAM2.1/Tiny_4_Layers.pt +0 -3
  32. SAM2.1/Tiny_5_Layers.pt +0 -3
  33. TS/M258.pt +3 -0
  34. TS/M258_1_Layers.pt +0 -3
  35. TS/M258_2_Layers.pt +0 -3
  36. TS/M258_3_Layers.pt +0 -3
  37. TS/M258_4_Layers.pt +0 -3
  38. TS/M258_5_Layers.pt +0 -3
  39. TS/M258_6_Layers.pt +0 -3
  40. TS/M258_7_Layers.pt +0 -3
  41. TS/M258_8_Layers.pt +0 -3
  42. TS/M291.pt +3 -0
  43. TS/M291_1_Layers.pt +0 -3
  44. TS/M291_2_Layers.pt +0 -3
  45. TS/M291_4_Layers.pt +0 -3
  46. TS/M291_5_Layers.pt +0 -3
  47. TS/M291_6_Layers.pt +0 -3
  48. TS/M291_7_Layers.pt +0 -3
  49. TS/M291_8_Layers.pt +0 -3
  50. TS/M292.pt +3 -0
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README.md CHANGED
@@ -31,11 +31,12 @@ This repository also includes example parameter maps, TorchScript model handling
31
  The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include:
32
 
33
  - **TotalSegmentator (TS)** — U-Net models trained for full-body anatomical segmentation
 
34
  - **Segment Anything 2.1 (SAM2.1)** — Foundation model for segmentation on natural images
35
  - **DINOv2** — Self-supervised vision transformer trained on diverse datasets
36
  - **Anatomix** — Transformer-based model with anatomical priors for medical images
37
 
38
- 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.
39
 
40
  In addition, the repository also includes:
41
 
@@ -44,20 +45,11 @@ In addition, the repository also includes:
44
 
45
  | Model | Specialization | Paper / Reference | Field of View | License | Preprocessing |
46
  |----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------|
47
- | **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 |
48
  | **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 |
49
- | **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.|
 
50
  | **Anatomix** | Anatomy-aware transformer encoder | [Dey et al., 2024](https://arxiv.org/abs/2411.02372) | Global(Static mode) | MIT | Normalize intensities to [0, 1] |
51
  | **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 |
52
 
53
-
54
- ---
55
-
56
- ### 🔍 TS Model Variants
57
-
58
- **TS Models** refer to the following TotalSegmentator-derived TorchScript models:
59
- `M258, M291, M293, M294, M295, M297, M298, M730, M731, M732, M733, M850, M851`
60
-
61
- Each model is specialized for a specific anatomical structure or resolution (e.g., 3mm / 6mm) and shares the same encoder-decoder architecture.
62
-
63
  ---
 
31
  The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include:
32
 
33
  - **TotalSegmentator (TS)** — U-Net models trained for full-body anatomical segmentation
34
+ - **MRSegmentator (MRSeg)** — U-Net models trained for full-body anatomical segmentation in MRI and CT
35
  - **Segment Anything 2.1 (SAM2.1)** — Foundation model for segmentation on natural images
36
  - **DINOv2** — Self-supervised vision transformer trained on diverse datasets
37
  - **Anatomix** — Transformer-based model with anatomical priors for medical images
38
 
39
+ Each model provides multiple feature extraction layers. This can be configured through the LayerMask parameter in the IMPACT configuration.
40
 
41
  In addition, the repository also includes:
42
 
 
45
 
46
  | Model | Specialization | Paper / Reference | Field of View | License | Preprocessing |
47
  |----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------|
48
+ | **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 | Normalize intensities to [0, 1] |
49
  | **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 |
50
+ | **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-M850–M853), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities + normalize model dependant |
51
+ | **MRSegmentator** | CT/MRI segmentation | [Häntze et al., 2024](https://arxiv.org/abs/2405.06463) | `2^l + 3` (l: layer number) | Apache 2.0 | Standardize intensities to zero mean and unit variance.|
52
  | **Anatomix** | Anatomy-aware transformer encoder | [Dey et al., 2024](https://arxiv.org/abs/2411.02372) | Global(Static mode) | MIT | Normalize intensities to [0, 1] |
53
  | **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 |
54
 
 
 
 
 
 
 
 
 
 
 
55
  ---
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