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Liver CT Segmentation

This project trains an nnUNet model to segment liver from CT scans. The model was trained on a dataset of 1565 CT scans from TotalSegmentator (N=1204) and FLARE21 (N=361) datasets. The model is used to generate annotations for liver in 89 CT scans from TCGA-LIHC collection.

The model_performance notebook contains the code to evaluate the model performance on the TCGA-LIHC collection against a validation evaluated by a radiologist and a non-expert.

Running the model

The pretrained model weights are available at zenodo. The simpliest way to use the model is to build and run the docker container.

Build container from pretrained weights

cd {REPO_DIR}/container
docker build -t bamf_liver_ct:latest .

Running inference

By default the container takes an input directory that contains DICOM files of CT scans, and an output directory where DICOM-SEG files will be placed. To run on multiple scans, place DICOM files for each scan in a separate folder within the input directory. The output directory will have a folder for each input scan, with the DICOM-SEG file inside.

example:

docker run --gpus all -v /path/to/input/dicoms:/data/input -v /path/for/output/dicoms:/data/output bamf_liver_ct:latest

There is an optional --nifti flag that will take nifti files as input and output.

Run inference on IDC Collections

This model was run on CT scans from the TCGA-LIHC collection. The AI segmentations and corrections by a radioloist for 10% of the dataset are available in the liver-ct.zip file on the zenodo record.

You can reproduce the results with the run_on_idc_data notebook on google colab.

Run inference on Medical Decathlon

Task 03 of the Medical Segmentation Decathlon is to segment liver and liver tumors from CT scans. We can use this dataset to evaluate our model.

  1. Download and extract Task03_Liver from the Medical Segmentation Decathlon. You should have a folder structure of {MSD_DIR}/Task03_Liver/imagesTr and {MSD_DIR}/Task03_Liver/labelsTr.
  2. Run the container with the following command, add the --nifti flag since inputs and outputs are nifti files.
docker run --gpus all -v ${MSD_DIR}/Task03_Liver/imagesTr:/data/input -v ${MSD_DIR}/Task03_Liver/predTr:/data/output bamf_liver_ct:latest --nifti

Training your own weights

Refer to the training instructions for more details.