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# Multi-organ segmentation in abdominal CT
### **Authors**
Chen Shen<sup>1</sup>, Holger R. Roth<sup>2</sup>, Kazunari Misawa<sup>3</sup>, Kensaku Mori<sup>1</sup>
1. Nagoya University, Japan
2. NVIDIA Corporation, USA
3. Aichi Cancer Center, Japan
### **Tags**
Segmentation, Multi-organ, Abdominal
## **Model Description**
This model uses the DiNTS model architecture searched on [Medical Segmentation Decathlon](http://medicaldecathlon.com/) Pancreas [1] and re-trained for multi-organ segmentation from abdominal CT images [2,3].
## **Data**
This model was trained on an abdominal CT dataset in portal venous phase collected from Aichi Cancer Center in Japan. Since this is a private dataset, similar models can be trained using other public multi-organ datasets like [BTCV](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480).
For this bundle, we split the 420 cases into training, validation and testing with 300, 60 and 60 cases, respectively.
## **Output**
8 channels
- 0: Background
- 1: Artery
- 2: Portal vein
- 3: Liver
- 4: Spleen
- 5: Stomach
- 6: Gallbladder
- 7: Pancreas
Here is an example of output.
## **Scores**
This model achieves the following Dice score on the validation data (our own split from the whole dataset):
Mean Dice = 88.6%
## MONAI Bundle Commands
In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.
For more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).
#### Execute model searching:
```
python -m scripts.search run --config_file configs/search.yaml
```
#### Execute multi-GPU model searching (recommended):
```
torchrun --nnodes=1 --nproc_per_node=8 -m scripts.search run --config_file configs/search.yaml
```
#### Execute training:
```
python -m monai.bundle run --config_file configs/train.yaml
```
Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:
```
python -m monai.bundle run --config_file configs/train.yaml
```
#### Override the `train` config to execute multi-GPU training:
```
torchrun --nnodes=1 --nproc_per_node=8 \
-m scripts.search run \
--config_file configs/search.yaml
```
#### Override the `train` config to execute evaluation with the trained model:
```
python -m monai.bundle run \
--config_file "['configs/train.yaml','configs/evaluate.yaml']"
```
#### Execute inference:
```
python -m monai.bundle run --config_file configs/inference.yaml
```
#### Export checkpoint for TorchScript:
```
python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.yaml
```
#### Execute inference with the TensorRT model:
```
python -m monai.bundle run --config_file "['configs/inference.yaml', 'configs/inference_trt.yaml']"
```
## **References**
[1] He, Y., Yang, D., Roth, H., Zhao, C. and Xu, D., 2021. Dints: Differentiable neural network topology search for 3d medical image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5841-5850).
[2] Roth, Holger R., et al. "A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2018.
[3] Shen, Chen, et al. "Effective hyperparameter optimization with proxy data for multi-organ segmentation." Medical Imaging 2022: Image Processing. Vol. 12032. SPIE, 2022.
## **License**
The Licensee is not allowed to distribute or make the model to any third party, either for free or for a fee. Reverse engineering of the model is not allowed. This includes, but is not limited to, providing the model as part of a commercial offering, sharing the model on a public or private network, or making the model available for download on the Internet.
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