Add the anatomix ViT encoder, trace both anatomix networks in evaluation mode

Anatomix/AnatomixDevViT.pt is the 32-feature PrimusV2 encoder anatomix-register.py uses by default; it takes a 128-voxel cube. Both anatomix exports are now traced in evaluation mode, so their normalisation layers read the learnt statistics, and wrapped in a scripted module, so their arguments keep their defaults and a caller may pass the image alone.

NOTICE and LICENSES carry the upstream terms: MIT for anatomix, Apache-2.0 for the dynamic-network-architectures blocks the ViT builds on.

Anatomix/Anatomix.pt

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LICENSES/anatomix-MIT.txt

@@ -0,0 +1,19 @@
+Copyright 2024 Neel Dey
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the “Software”), to deal in
+the Software without restriction, including without limitation the rights to
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSES/dynamic-network-architectures-Apache-2.0.txt

@@ -0,0 +1,201 @@
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NOTICE

@@ -0,0 +1,27 @@
+Third-party material redistributed in this repository
+=====================================================
+
+Anatomix/Anatomix.pt, Anatomix/AnatomixDevViT.pt
+------------------------------------------------
+TorchScript exports of the anatomix pretrained networks.
+
+  Weights:  https://huggingface.co/neeldey/anatomix (anatomix.pth, anatomix-dev-vit.pth)
+  Code:     https://github.com/neel-dey/anatomix
+  License:  MIT, Copyright 2024 Neel Dey -- see LICENSES/anatomix-MIT.txt
+  Paper:    Dey et al., Learning General-purpose Biomedical Volume Representations
+            using Randomized Synthesis, ICLR 2025, arXiv:2411.02372
+
+AnatomixDevViT additionally builds on PrimusV2 from dynamic-network-architectures,
+which anatomix configures and extends (output normalisation, register-token
+initialisation, QK normalisation):
+
+  Code:     https://github.com/MIC-DKFZ/dynamic-network-architectures
+  License:  Apache License 2.0, Copyright 2022 Division of Medical Image Computing,
+            German Cancer Research Center (DKFZ) -- see
+            LICENSES/dynamic-network-architectures-Apache-2.0.txt
+
+Changes made here: each network is traced in evaluation mode and wrapped in a
+scripted module that normalises the input from the statistics IMPACT passes and
+returns one tensor per feature layer. The weights themselves are unchanged. The
+export scripts are published in https://github.com/vboussot/ImpactLoss under
+Data/Models/builds/Anatomix/.

README.md

@@ -26,6 +26,34 @@ This repository also includes example parameter maps, TorchScript model handling
 
 ---
 
+## Anatomix variants
+
+Two exports of the [anatomix](https://github.com/neel-dey/anatomix) encoders:
+
+| File | Network | Feature channels | Input size |
+|---|---|---|---|
+| `Anatomix/Anatomix.pt` | U-Net, 4 levels (`anatomix.pth`) | 16 | any (multiple of 16 per axis) |
+| `Anatomix/AnatomixDevViT.pt` | PrimusV2 ViT, 27.1 M parameters (`anatomix-dev-vit.pth`) | 32 | **128 x 128 x 128 only** |
+
+The ViT's positional embedding is sized for a 128-voxel cube, so it has to be scored patch by patch at
+that size: `PatchSize 128 128 128` for Elastix, `feature_patch: 128` for the KonfAI FireANTs engine.
+
+Both are traced with their network in **evaluation mode**, so their normalisation layers use the learnt
+statistics, and wrapped in a scripted module, so `nb_layers`, `stats` and `direction` keep their default
+values and a caller may pass only the image. `Anatomix.pt` was previously traced with the network in
+training mode, where its BatchNorms read each batch's own statistics: its features spanned roughly
++-109 against +-5 now, and the two correlate at 0.21 on random input. Pin the revision before
+2026-09-23 to reproduce a result obtained with it:
+
+```python
+ImpactModelConfiguration("VBoussot/impact-torchscript-models:Anatomix/Anatomix.pt", revision="<sha>")
+```
+
+Redistribution terms and the export scripts are listed in `NOTICE`; the scripts themselves live in
+[ImpactLoss/Data/Models/builds/Anatomix](https://github.com/vboussot/ImpactLoss/tree/main/Data/Models/builds/Anatomix).
+
+---
+
 ## 📚 Pretrained Model 
 
 The TorchScript models provided in this repository were exported from publicly available pretrained networks. These include:
@@ -49,7 +77,8 @@ In addition, the repository also includes:
 | **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-M850–M853), standardize intensities to zero mean and unit variance. For CT models (e.g., TS/M258, TS/M291), clip intensities + normalize model dependant |
 | **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.|
-| **Anatomix**   | Anatomy-aware transformer encoder     | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)        | Global(Static mode)          | MIT           | Normalize intensities to [0, 1] |
+| **Anatomix**   | Anatomy-aware encoder (U-Net, 16 features) | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)   | Global(Static mode)          | MIT           | Normalize intensities to [0, 1] |
+| **AnatomixDevViT** | Anatomy-aware encoder (ViT, 32 features) | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)  | 128 (fixed input)     | MIT + Apache 2.0 | 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 |
 
 ---

models.json

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-    "fov": {
-      "formula": "2^l+3"
-    },
-    "dimension": "2",
-    "numberofchannels": "3"
-  }
+    "Anatomix/Anatomix.pt": {
+        "fov": {
+            "formula": "Global(Static mode)"
+        },
+        "dimension": "3",
+        "numberofchannels": "1"
+    },
+    "Anatomix/AnatomixDevViT.pt": {
+        "fov": {
+            "formula": "128"
+        },
+        "dimension": "3",
+        "numberofchannels": "1"
+    },
+    "Dino/DinoV2_Small.pt": {
+        "fov": {
+            "formula": "14"
+        },
+        "dimension": "2",
+        "numberofchannels": "3"
+    },
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