metadata

tags:
  - monai
  - medical
library_name: monai
license: apache-2.0

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].

The architecture of Swin UNETR is shown as below:

Data

The training data is from the BTCV dataset (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 Kidney, 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

The figure shows the training loss curve for 10K iterations.

drawing

A graph showing the validation mean Dice for 5000 epochs.

drawing

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

Mean Dice = 0.8269

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

Please note that the distributed training related options depend on the actual running environment, thus you may need to remove --standalone, modify --nnodes or do some other necessary changes according to the machine you used. Please refer to pytorch's official tutorial for more details.

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).

License

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.