The liver annotations from AMOS (N=40) and DUKE Liver Dataset V2 (N=310) dataset was used to train a model for liver segmentation . The combined training dataset includes 350 cases of annotated livers in T1 MR scans. An ensemble of fivefold cross-validation within the nnUNet framework is used for automatic liver segmentation. The model is used to generate annotations for liver in 67 MR 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 set evaluated by a radiologist and a non-expert.
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By default the container takes an input directory that contains DICOM files of MR 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:
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There is an optional --nifti flag that will take nifti files as input and output.
This model was run on MR scans from the TCGA-LIHC collection. The AI segmentations and corrections by a radioloist for 10% of the dataset are available in the liver-mr.zip file on the zenodo record
- TODO: You can reproduce the results with the run_on_idc_data notebook on google colab.
- TODO: Refer to the training instructions for more details.