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].

Data

The training data is from the BTCV dataset (Register through Synapse and download the Abdomen/RawData.zip).

• Target: Multi-organs
• Task: Segmentation
• Modality: CT
• Size: 30 3D volumes (24 Training + 6 Testing)

Preprocessing

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

Training configuration

The training as performed with the following:

• GPU: At least 32GB of GPU memory
• Actual Model Input: 96 x 96 x 96
• AMP: True
• Optimizer: Adam
• Learning Rate: 2e-4

Memory Consumption

• Dataset Manager: CacheDataset
• Data Size: 30 samples
• Cache Rate: 1.0
• Single GPU - System RAM Usage: 5.8G

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Input

1 channel

• CT image

Output

14 channels:

• 0: Background
• 1: Spleen
• 2: Right Kidney
• 3: Left Kideny
• 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

Dice score was used for evaluating the performance of the model. This model achieves a mean dice score of 0.82

Training Loss

Validation Dice

TensorRT speedup

The swin_unetr bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

Where:

• model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
• end2end means run the bundle end-to-end with the TensorRT based model.
• torch_tf32 and torch_amp are for the PyTorch models with or without amp mode.
• trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
• speedup amp, speedup tf32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
• amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

• TensorRT: 10.3.0+cuda12.6
• Torch-TensorRT Version: 2.4.0
• CPU Architecture: x86-64
• OS: ubuntu 20.04
• Python version:3.10.12
• CUDA version: 12.6
• GPU models and configuration: A100 80G

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.

Execute training:

python -m monai.bundle run --config_file configs/train.json

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.json --dataset_dir <actual dataset path>

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.

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

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"

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

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.