dnouri/brats_mri_segmentation

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.

Workflow

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.

Data

The training data is from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018.

• 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.

Please run scripts/prepare_datalist.py to produce the data list. The command is like:

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

Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)

1. Normalizing to unit std with zero mean
2. Randomly cropping to (224, 224, 144)
3. Randomly spatial flipping
4. Randomly scaling and shifting intensity of the volume

Output

Output: 3 channels

• Label 0: TC tumor subregion
• Label 1: WT tumor subregion
• Label 2: ET tumor subregion

Model Performance

The achieved Dice scores on the validation data are:

• Tumor core (TC): 0.8559
• Whole tumor (WT): 0.9026
• Enhancing tumor (ET): 0.7905
• Average: 0.8518

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=8 -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

Disclaimer

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

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.