readme: ---
  tags:
  - MONAI
  ---
  # Description
  A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
  
  # Model Overview
  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.
  
  ## Data
  The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
  
  ## Training configuration
  The training was performed with at least 12GB-memory GPUs.
  
  Actual Model Input: 96 x 96 x 96
  
  ## Input and output formats
  Input: 1 channel CT image
  
  Output: 2 channels: Label 1: spleen; Label 0: everything else
  
  ## Scores
  This model achieves the following Dice score on the validation data (our own split from the training dataset):
  
  Mean Dice = 0.96
  
  ## 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
  ```
  
  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] Xia, Yingda, et al. "3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training." arXiv preprint arXiv:1811.12506 (2018). https://arxiv.org/abs/1811.12506.
  
  [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. https://doi.org/10.1007/978-3-030-12029-0_40
  