| # Example: inference with pretrained nnU-Net models | |
| This is a step-by-step example of how to run inference with pretrained nnU-Net models on the Prostate dataset of the | |
| Medical Segemtnation Decathlon. | |
| 1) Install nnU-Net by following the instructions [here](../readme.md#installation). Make sure to set all relevant paths, | |
| also see [here](setting_up_paths.md). This step is necessary so that nnU-Net knows where to store trained models. | |
| 2) Download the Prostate dataset of the Medical Segmentation Decathlon from | |
| [here](https://drive.google.com/drive/folders/1HqEgzS8BV2c7xYNrZdEAnrHk7osJJ--2). Then extract the archive to a | |
| destination of your choice. | |
| 3) We selected the Prostate dataset for this example because we have a utility script that converts the test data into | |
| the correct format. | |
| Decathlon data come as 4D niftis. This is not compatible with nnU-Net (see dataset format specified | |
| [here](dataset_conversion.md)). Convert the Prostate dataset into the correct format with | |
| ```bash | |
| nnUNet_convert_decathlon_task -i /xxx/Task05_Prostate | |
| ``` | |
| Note that `Task05_Prostate` must be the folder that has the three 'imagesTr', 'labelsTr', 'imagesTs' subfolders! | |
| The converted dataset can be found in `$nnUNet_raw_data_base/nnUNet_raw_data` ($nnUNet_raw_data_base is the folder for | |
| raw data that you specified during installation) | |
| 4) Download the pretrained model using this command: | |
| ```bash | |
| nnUNet_download_pretrained_model Task005_Prostate | |
| ``` | |
| 5) The prostate dataset requires two image modalities as input. This is very much liKE RGB images have three color channels. | |
| nnU-Net recognizes modalities by the file ending: a single test case of the prostate dataset therefore consists of two files | |
| `case_0000.nii.gz` and `case_0001.nii.gz`. Each of these files is a 3D image. The file ending with 0000.nii.gz must | |
| always contain the T2 image and 0001.nii.gz the ADC image. Whenever you are using pretrained models, you can use | |
| ```bash | |
| nnUNet_print_pretrained_model_info Task005_Prostate | |
| ``` | |
| to obtain information on which modality needs to get which number. The output for Prostate is the following: | |
| Prostate Segmentation. | |
| Segmentation targets are peripheral and central zone, | |
| input modalities are 0: T2, 1: ADC. | |
| Also see Medical Segmentation Decathlon, http://medicaldecathlon.com/ | |
| 6) The script we ran in 3) automatically converted the test data for us and stored them in | |
| `$nnUNet_raw_data_base/nnUNet_raw_data/Task005_Prostate/imagesTs`. Note that you need to do this conversion youself when | |
| using other than Medcial Segmentation Decathlon datasets. No worries. Doing this is easy (often as simple as appending | |
| a _0000 to the file name if only one input modality is required). Instructions can be found here [here](data_format_inference.md). | |
| 7) You can now predict the Prostate test cases with the pretrained model. We exemplarily use the 3D full resoltion U-Net here: | |
| ```bash | |
| nnUNet_predict -i $nnUNet_raw_data_base/nnUNet_raw_data/Task005_Prostate/imagesTs/ -o OUTPUT_DIRECTORY -t 5 -m 3d_fullres | |
| ``` | |
| Note that `-t 5` specifies the task with id 5 (which corresponds to the Prostate dataset). You can also give the full | |
| task name `Task005_Prostate`. `OUTPUT_DIRECTORY` is where the resulting segmentations are saved. | |
| Predictions should be quite fast and you should be done within a couple of minutes. If you would like to speed it | |
| up (at the expense of a slightly lower segmentation quality) you can disable test time data augmentation by | |
| setting the `--disable_tta` flag (8x speedup). If this is still too slow for you, you can consider using only a | |
| single model instead of the ensemble by specifying `-f 0`. This will use only the model trained on fold 0 of the | |
| cross-validation for another 5x speedup. | |
| 8) If you want to use an ensemble of different U-Net configurations for inference, you need to run the following commands: | |
| Prediction with 3d full resolution U-Net (this command is a little different than the one above). | |
| ```bash | |
| nnUNet_predict -i $nnUNet_raw_data_base/nnUNet_raw_data/Task005_Prostate/imagesTs/ -o OUTPUT_DIRECTORY_3D -t 5 --save_npz -m 3d_fullres | |
| ``` | |
| Prediction with 2D U-Net | |
| ```bash | |
| nnUNet_predict -i $nnUNet_raw_data_base/nnUNet_raw_data/Task005_Prostate/imagesTs/ -o OUTPUT_DIRECTORY_2D -t 5 --save_npz -m 2d | |
| ``` | |
| `--save_npz` will tell nnU-Net to also store the softmax probabilities for ensembling. | |
| You can then merge the predictions with | |
| ```bash | |
| nnUNet_ensemble -f OUTPUT_DIRECTORY_3D OUTPUT_DIRECTORY_2D -o OUTPUT_FOLDER_ENSEMBLE -pp POSTPROCESSING_FILE | |
| ``` | |
| This will merge the predictions from `OUTPUT_DIRECTORY_2D` and `OUTPUT_DIRECTORY_3D`. `-pp POSTPROCESSING_FILE` | |
| (optional!) is a file that gives nnU-Net information on how to postprocess the ensemble. These files were also | |
| downloaded as part of the pretrained model weights and are located at `RESULTS_FOLDER/nnUNet/ensembles/ | |
| Task005_Prostate/ensemble_2d__nnUNetTrainerV2__nnUNetPlansv2.1--3d_fullres__nnUNetTrainerV2__nnUNetPlansv2.1/postprocessing.json`. | |
| We will make the postprocessing files more accessible in a future (soon!) release. | |