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--- |
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tags: |
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- monai |
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- medical |
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library_name: monai |
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license: unknown |
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--- |
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# Description |
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A pre-trained model for inferencing volumetric (3D) kidney substructures segmentation from contrast-enhanced CT images (Arterial/Portal Venous Phase). |
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A tutorial and release of model for kidney cortex, medulla and collecting system segmentation. |
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Authors: Yinchi Zhou (yinchi.zhou@vanderbilt.edu) | Xin Yu (xin.yu@vanderbilt.edu) | Yucheng Tang (yuchengt@nvidia.com) | |
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# Model Overview |
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A pre-trained UNEST base model [1] for volumetric (3D) renal structures segmentation using dynamic contrast enhanced arterial or venous phase CT images. |
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## Data |
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The training data is from the [ImageVU RenalSeg dataset] from Vanderbilt University and Vanderbilt University Medical Center. |
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(The training data is not public available yet). |
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- Target: Renal Cortex | Medulla | Pelvis Collecting System |
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- Task: Segmentation |
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- Modality: CT (Artrial | Venous phase) |
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- Size: 96 3D volumes |
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The data and segmentation demonstration is as follow: |
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![](./renal.png) <br> |
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## Method and Network |
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The UNEST model is a 3D hierarchical transformer-based semgnetation network. |
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Details of the architecture: |
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![](./unest.png) <br> |
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## Training configuration |
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The training was performed with at least one 16GB-memory GPU. |
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Actual Model Input: 96 x 96 x 96 |
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## Input and output formats |
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Input: 1 channel CT image |
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Output: 4: 0:Background, 1:Renal Cortex, 2:Medulla, 3:Pelvicalyceal System |
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## Performance |
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A graph showing the validation mean Dice for 5000 epochs. |
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![](./val_dice.png) <br> |
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This model achieves the following Dice score on the validation data (our own split from the training dataset): |
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Mean Valdiation Dice = 0.8523 |
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Note that mean dice is computed in the original spacing of the input data. |
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## commands example |
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Download trained checkpoint model to ./model/model.pt: |
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Add scripts component: To run the workflow with customized components, PYTHONPATH should be revised to include the path to the customized component: |
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``` |
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export PYTHONPATH=$PYTHONPATH:"'<path to the bundle root dir>/scripts'" |
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``` |
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Execute inference: |
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``` |
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python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf |
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``` |
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## More examples output |
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![](./demos.png) <br> |
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# Disclaimer |
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This is an example, not to be used for diagnostic purposes. |
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# References |
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[1] Yu, Xin, Yinchi Zhou, Yucheng Tang et al. "Characterizing Renal Structures with 3D Block Aggregate Transformers." arXiv preprint arXiv:2203.02430 (2022). https://arxiv.org/pdf/2203.02430.pdf |
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[2] Zizhao Zhang et al. "Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding." AAAI Conference on Artificial Intelligence (AAAI) 2022 |
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