Update README.md

README.md

@@ -39,16 +39,17 @@ Each model provides multiple feature extraction layers, which can be selected in
 
 In addition, the repository also includes:
 
-- **MIND** — A handcrafted Modality Independent Neighborhood Descriptor, wrapped in TorchScript
+- **MIND** — A handcrafted descriptor, wrapped in TorchScript
 
 
-| Model          | Specialization                        | Paper / Reference                                           | Field of View         | License      |
-|----------------|----------------------------------------|-------------------------------------------------------------|------------------------|--------------|
-| **MIND**       | Handcrafted descriptor                | [Heinrich et al., 2012](https://doi.org/10.1016/j.media.2012.05.008) | `2r + 1`               | Research only |
-| **SAM2.1**     | General segmentation (natural images) | [Ravi et al., 2023](https://arxiv.org/abs/2408.00714)   | 29                    | MIT           |
-| **TS Models**  | Multi-resolution CT/MRI segmentation  | [Wasserthal et al., 2022](https://arxiv.org/abs/2208.05868)    | `2^l + 3`             | Apache 2.0    |
-| **Anatomix**   | Anatomy-aware transformer encoder     | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)   | Hierarchical          | MIT           |
-| **DINOv2**     | Self-supervised vision transformer    | [Oquab et al., 2023](https://arxiv.org/abs/2304.07193)      | Global / ViT-Base     | MIT           |
+| Model          | Specialization                        | Paper / Reference                                           | Field of View         | License      | Preprocessing |
+|----------------|---------------------------------------|-------------------------------------------------------------|------------------------|--------------|---------------|
+| **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 |
+| **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), 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.|
+| **Anatomix**   | Anatomy-aware transformer encoder     | [Dey et al., 2024](https://arxiv.org/abs/2411.02372)        | Global(Static mode)          | MIT           | 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 |
+
 
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