# Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.

# Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].
## Data
The training data is from the [BTCV dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480/) (Please regist in `Synapse` and download the `Abdomen/RawData.zip`).
The dataset format needs to be redefined using the following commands:

```
unzip RawData.zip
mv RawData/Training/img/ RawData/imagesTr
mv RawData/Training/label/ RawData/labelsTr
mv RawData/Testing/img/ RawData/imagesTs
```

- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing)

## Training configuration
The training was performed with at least 32GB-memory GPUs.

Actual Model Input: 96 x 96 x 96

## Input and output formats
Input: 1 channel CT image

Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland

## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.8283

Note that mean dice is computed in the original spacing of the input data.
## commands example
Execute training:

```
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
```

Override the `train` config to execute multi-GPU training:

```
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
```

Override the `train` config to execute evaluation with the trained model:

```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
```

Execute inference:

```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
```

Export checkpoint to TorchScript file:

TorchScript conversion is currently not supported.

# Disclaimer
This is an example, not to be used for diagnostic purposes.

# References
[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.

[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.

[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).

{"tags": ["MONAI"]}
MONAI arxiv:2201.01266 arxiv:2111.14791 region:us
TAGS
#MONAI #arxiv-2201.01266 #arxiv-2111.14791 #region-us 

# Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.

# Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].
## Data
The training data is from the BTCV dataset (Please regist in 'Synapse' and download the 'Abdomen/URL').
The dataset format needs to be redefined using the following commands:



- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing)

## Training configuration
The training was performed with at least 32GB-memory GPUs.

Actual Model Input: 96 x 96 x 96

## Input and output formats
Input: 1 channel CT image

Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland

## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.8283

Note that mean dice is computed in the original spacing of the input data.
## commands example
Execute training:



Override the 'train' config to execute multi-GPU training:



Override the 'train' config to execute evaluation with the trained model:



Execute inference:



Export checkpoint to TorchScript file:

TorchScript conversion is currently not supported.

# Disclaimer
This is an example, not to be used for diagnostic purposes.

# References
[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). URL

[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). URL

[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).

# Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image. # Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3]. ## Data
The training data is from the BTCV dataset (Please regist in 'Synapse' and download the 'Abdomen/URL').
The dataset format needs to be redefined using the following commands:



- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing) ## Training configuration
The training was performed with at least 32GB-memory GPUs.

Actual Model Input: 96 x 96 x 96 ## Input and output formats
Input: 1 channel CT image

Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland ## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.8283

Note that mean dice is computed in the original spacing of the input data. ## commands example
Execute training:



Override the 'train' config to execute multi-GPU training:



Override the 'train' config to execute evaluation with the trained model:



Execute inference:



Export checkpoint to TorchScript file:

TorchScript conversion is currently not supported. # Disclaimer
This is an example, not to be used for diagnostic purposes. # References
[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). URL

[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). URL

[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).
TAGS
#MONAI #arxiv-2201.01266 #arxiv-2111.14791 #region-us 
 # Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image. # Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3]. ## Data
The training data is from the BTCV dataset (Please regist in 'Synapse' and download the 'Abdomen/URL').
The dataset format needs to be redefined using the following commands:



- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing) ## Training configuration
The training was performed with at least 32GB-memory GPUs.

Actual Model Input: 96 x 96 x 96 ## Input and output formats
Input: 1 channel CT image

Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland ## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.8283

Note that mean dice is computed in the original spacing of the input data. ## commands example
Execute training:



Override the 'train' config to execute multi-GPU training:



Override the 'train' config to execute evaluation with the trained model:



Execute inference:



Export checkpoint to TorchScript file:

TorchScript conversion is currently not supported. # Disclaimer
This is an example, not to be used for diagnostic purposes. # References
[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). URL

[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). URL

[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).
passage: TAGS
#MONAI #arxiv-2201.01266 #arxiv-2111.14791 #region-us 
# Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.# Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].## Data
The training data is from the BTCV dataset (Please regist in 'Synapse' and download the 'Abdomen/URL').
The dataset format needs to be redefined using the following commands:



- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing)## Training configuration
The training was performed with at least 32GB-memory GPUs.

Actual Model Input: 96 x 96 x 96## Input and output formats
Input: 1 channel CT image

Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland## Performance
A graph showing the validation mean Dice for 5000 epochs.

![](./val_dice.png) <br>

This model achieves the following Dice score on the validation data (our own split from the training dataset):

Mean Dice = 0.8283

Note that mean dice is computed in the original spacing of the input data.## commands example
Execute training:



Override the 'train' config to execute multi-GPU training:



Override the 'train' config to execute evaluation with the trained model:



Execute inference:



Export checkpoint to TorchScript file:

TorchScript conversion is currently not supported.# Disclaimer
This is an example, not to be used for diagnostic purposes.