README.md

---
tags:
- monai
- medical
library_name: monai
license: apache-2.0
---
# Model Overview
A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation).

The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).
- The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c.
- The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
-  The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.

![Model workflow](https://developer.download.nvidia.com/assets/Clara/Images/clara_pt_brain_mri_segmentation_workflow.png)

## Data
The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/cbica/sbia/brats2018/tasks.html).

- Target: 3 tumor subregions
- Task: Segmentation
- Modality: MRI
- Size: 285 3D volumes (4 channels each)

The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.

### Preprocessing
The data list/split can be created with the script `scripts/prepare_datalist.py`.

```
python scripts/prepare_datalist.py --path your-brats18-dataset-path
```

## Training configuration
This model utilized a similar approach described in 3D MRI brain tumor segmentation using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:

- GPU: At least 16GB of GPU memory.
- Actual Model Input: 224 x 224 x 144
- AMP: True
- Optimizer: Adam
- Learning Rate: 1e-4
- Loss: DiceLoss

## Input
4 channel aligned MRIs at 1 x 1 x 1 mm
- T1c
- T1
- T2
- FLAIR

## Output
3 channels
- Label 0: TC tumor subregion
- Label 1: WT tumor subregion
- Label 2: ET tumor subregion

## Performance
Dice score was used for evaluating the performance of the model. This model achieved Dice scores on the validation data of:
- Tumor core (TC): 0.8559
- Whole tumor (WT): 0.9026
- Enhancing tumor (ET): 0.7905
- Average: 0.8518

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.

#### Training Loss and Dice
![A graph showing the training loss and the mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_train.png)

#### Validation Dice
![A graph showing the validation mean dice over 300 epochs](https://developer.download.nvidia.com/assets/Clara/Images/monai_brats_mri_segmentation_val.png)

## 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=8 -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']"
```

#### Execute inference:

```
python -m monai.bundle run --config_file configs/inference.json
```

# References
[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.

# 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,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.