---
tags:
- monai
- medical
library_name: monai
license: apache-2.0
---
# Model Overview
Body CT segmentation models are evolving. Starting from abdominal multi-organ segmentation model [1]. Now the community is developing hundreds of target anatomies. In this bundle, we provide re-trained models for (3D) segmentation of 104 whole-body segments.
This model is trained using the SegResNet [3] network. The model is trained using TotalSegmentator datasets [2].
Figure source from the TotalSegmentator [2].
### MONAI Label Showcase
- We highlight the use of this bundle to use and visualize in MONAI Label + 3D Slicer integration.
## Data
The training set is the 104 whole-body structures from the TotalSegmentator released datasets. Users can find more details on the datasets at https://github.com/wasserth/TotalSegmentator. All rights and licenses are reserved to the original authors.
- Target: 104 structures
- Modality: CT
- Source: TotalSegmentator
- Challenge: Large volumes of structures in CT images
### Preprocessing
To use the bundle, users need to download the data and merge all annotated labels into one NIFTI file. Each file contains 0-104 values, each value represents one anatomy class. A sample set is provided with this [link](https://drive.google.com/file/d/1DtDmERVMjks1HooUhggOKAuDm0YIEunG/view?usp=share_link).
## Training Configuration
The segmentation of 104 tissues is formulated as voxel-wise multi-label segmentation. The model is optimized with the gradient descent method minimizing Dice + cross-entropy loss between the predicted mask and ground truth segmentation.
The training was performed with the following:
- GPU: 32 GB of GPU memory
- Actual Model Input: 96 x 96 x 96
- AMP: True
- Optimizer: AdamW
- Learning Rate: 1e-4
- Loss: DiceCELoss
### Input
One channel
- CT image
### Output
105 channels
- Label 0: Background (everything else)
- label 1-105: Foreground classes (104)
## Resource Requirements and Latency Benchmarks
### High-Resolution and Low-Resolution Models
We retrained two versions of the totalSegmentator models, following the original paper and implementation.
To meet multiple demands according to computation resources and performance, we provide a 1.5 mm model and a 3.0 mm model, both models are trained with 104 foreground output channels.
In this bundle, we configured a parameter called `highres`, users can set it to `true` when using 1.5 mm model, and set it to `false` to use the 3.0 mm model. The high-resolution model is named `model.pt` by default, the low-resolution model is named `model_lowres.pt`.
In MONAI Label use case, users can set the parameter in 3D Slicer plugin to control which model to infer and train.
- Pretrained Checkpoints
- 1.5 mm model: [Download link](https://drive.google.com/file/d/1PHpFWboimEXmMSe2vBra6T8SaCMC2SHT/view?usp=share_link)
- 3.0 mm model: [Download link](https://drive.google.com/file/d/1c3osYscnr6710ObqZZS8GkZJQlWlc7rt/view?usp=share_link)
Latencies and memory performance of using the bundle with MONAI Label:
Tested Image Dimension: **(512, 512, 397)**, the slice thickness is **1.5mm** in this case. After resample to **1.5** isotropic resolution, the dimension is **(287, 287, 397)**
### 1.5 mm (highres) model (Single Model with 104 foreground classes)
Benchmarking on GPU: Memory: **28.73G**
- `++ Latencies => Total: 6.0277; Pre: 1.6228; Inferer: 4.1153; Invert: 0.0000; Post: 0.0897; Write: 0.1995`
Benchmarking on CPU: Memory: **26G**
- `++ Latencies => Total: 38.3108; Pre: 1.6643; Inferer: 30.3018; Invert: 0.0000; Post: 6.1656; Write: 0.1786`
### 3.0 mm (lowres) model (single model with 104 foreground classes)
GPU: Memory: **5.89G**
- `++ Latencies => Total: 1.9993; Pre: 1.2363; Inferer: 0.5207; Invert: 0.0000; Post: 0.0358; Write: 0.2060`
CPU: Memory: **2.3G**
- `++ Latencies => Total: 6.6138; Pre: 1.3192; Inferer: 3.6746; Invert: 0.0000; Post: 1.4431; Write: 0.1760`
## Performance
### 1.5 mm Model Training
#### Training Accuracy
#### Validation Dice
Please note that this bundle is non-deterministic because of the trilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.
Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.
## 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 training:
```
python -m monai.bundle run --config_file configs/train.json
```
#### Override the `train` config to execute multi-GPU training:
```
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/multi_gpu_train.json']"
```
Please note that the distributed training-related options depend on the actual running environment; thus, users may need to remove `--standalone`, modify `--nnodes`, or do some other necessary changes according to the machine used. For more details, please refer to [pytorch's official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
#### Override the `train` config to execute evaluation with the trained model:
```
python -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json']"
```
#### Override the `train` config and `evaluate` config to execute multi-GPU evaluation:
```
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file "['configs/train.json','configs/evaluate.json','configs/multi_gpu_evaluate.json']"
```
#### Execute inference:
```
python -m monai.bundle run --config_file configs/inference.json
```
#### Execute inference with Data Samples:
```
python -m monai.bundle run --config_file configs/inference.json --datalist "['sampledata/imagesTr/s0037.nii.gz','sampledata/imagesTr/s0038.nii.gz']"
```
# References
[1] Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894.
[2] Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.
[3] Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.
# License
Copyright (c) MONAI Consortium
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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