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- Cosmin Ciausu (Brigham and Women's Hospital)
- Andrey Fedorov (Brigham and Women's Hospital)
Inference on prostate IDC data using nnUnet segmentation framework.
This framework provides an end to end segmentation pipeline, from pre-processing, data augmentation, hyper-parameter selection to post-processing, with variants of an Unet segmentation model.
The Imaging Data Commons platform provides both labelled and unlabelled prostate scans collections.
- Augment existing and applicable IDC collections with prostate segmentations obtained from nnUnet pre-trained models.
- Investigate/analyze nnUnet framework generalizability to IDC data.
- Use nnUnet models pre-trained on prostate data to get predictions results on labelled IDC collections first, PROSTATEx or qin-prostate-repeatability for example.
- Evaluate performance on labelled data, followed by inference on unlabelled IDC collections.
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Verification of nnUnet claimed results on prostate decathlon data, used for available pre-trained models.
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Inference on Qin-Prostate-Repeatability collection using a pre-trained nnUnet model on task05 imaging decathlon data :
- 3d full-res model, T2 and ADC modalities, so there is a need to resample the input.
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Inference on Qin-Prostate-Repeatability collection using a different pre-trained nnUnet model, task 24 promise.
- Easier to corner the resampling problem since this pre-trained model has only one input modality -- T2
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Obtained good dice scores results on the 15 PatientID divided into two studies each using this model.
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Dealt with the Resampling/Converting issue - slice spacing incorrect -- use of simpleITK instead of plastimatch
Slicer visualisation of ground truth and predicted whole prostate segmentation mask, on PatientID01. Red is ground truth and green is prediction from nnUnet.
