A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
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.
The training dataset is Task09_Spleen.tar from http://medicaldecathlon.com/.
The training was performed with at least 12GB-memory GPUs.
Actual Model Input: 96 x 96 x 96
Input: 1 channel CT image
Output: 2 channels: Label 1: spleen; Label 0: everything else
This model achieves the following Dice score on the validation data (our own split from the training dataset):
Mean Dice = 0.96
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
Verify the metadata format:
python -m monai.bundle verify_metadata --meta_file configs/metadata.json --filepath eval/schema.json
Verify the data shape of network:
python -m monai.bundle verify_net_in_out network_def --meta_file configs/metadata.json --config_file configs/inference.json
Export checkpoint to TorchScript file:
python -m monai.bundle ckpt_export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json
This is an example, not to be used for diagnostic purposes.
[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