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 inference:

python -m monai.bundle run evaluator --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 export network_def --filepath models/model.ts --ckpt_file models/model.pt --meta_file configs/metadata.json --config_file configs/inference.json

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