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A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
Latest Version
October 19, 2023
33.97 MB

Model Overview

A pre-trained model for volumetric (3D) segmentation of the spleen from CT images.

This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.

model workflow


The training dataset is the Spleen Task from the Medical Segmentation Decathalon. Users can find more details on the datasets at

  • Target: Spleen
  • Modality: CT
  • Size: 61 3D volumes (41 Training + 20 Testing)
  • Source: Memorial Sloan Kettering Cancer Center
  • Challenge: Large-ranging foreground size

Training configuration

The segmentation of spleen region is formulated as the voxel-wise binary classification. Each voxel is predicted as either foreground (spleen) or background. And the model is optimized with gradient descent method minimizing Dice + cross entropy loss between the predicted mask and ground truth segmentation.

The training was performed with the following:

  • GPU: at least 12GB of GPU memory
  • Actual Model Input: 96 x 96 x 96
  • AMP: True
  • Optimizer: Novograd
  • Learning Rate: 0.002
  • Loss: DiceCELoss
  • Dataset Manager: CacheDataset

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.


One channel

  • CT image


Two channels

  • Label 1: spleen
  • Label 0: everything else


Dice score is used for evaluating the performance of the model. This model achieves a mean dice score of 0.961.

Training Loss

A graph showing the training loss over 1260 epochs (10080 iterations).

Validation Dice

A graph showing the validation mean Dice over 1260 epochs.

TensorRT speedup

The spleen_ct_segmentation bundle supports acceleration with TensorRT through the ONNX-TensorRT method. The table below displays the speedup ratios observed on an A100 80G GPU.

method torch_fp32(ms) torch_amp(ms) trt_fp32(ms) trt_fp16(ms) speedup amp speedup fp32 speedup fp16 amp vs fp16
model computation 6.46 4.48 2.52 1.96 1.44 2.56 3.30 2.29
end2end 1268.03 1152.40 1137.40 1114.25 1.10 1.11 1.14 1.03


  • 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_fp32 and torch_amp are for the PyTorch models with or without amp mode.
  • trt_fp32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
  • speedup amp, speedup fp32 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.

Currently, the only available method to accelerate this model is through ONNX-TensorRT. However, the Torch-TensorRT method is under development and will be available in the near future.

This result is benchmarked under:

  • TensorRT: 8.5.3+cuda11.8
  • Torch-TensorRT Version: 1.4.0
  • CPU Architecture: x86-64
  • OS: ubuntu 20.04
  • Python version:3.8.10
  • CUDA version: 12.1
  • 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']"
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
Export checkpoint to TensorRT based models with fp32 or fp16 precision:
python -m monai.bundle trt_export --net_id network_def --filepath models/model_trt.ts --ckpt_file models/ --meta_file configs/metadata.json --config_file configs/inference.json --precision <fp32/fp16> --dynamic_batchsize "[1, 4, 8]" --use_onnx "True" --use_trace "True"
Execute inference with the TensorRT model:
python -m monai.bundle run --config_file "['configs/inference.json', 'configs/inference_trt.json']"


[1] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018).

[2] Kerfoot E., Clough J., Oksuz I., Lee J., King A.P., Schnabel J.A. (2019) Left-Ventricle Quantification Using Residual U-Net. In: Pop M. et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science, vol 11395. Springer, Cham.


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