# How to generate lesion measures from the test set predicted masks?

Once you have performed the inference and saved the network predicted masks in NIFTI format (as described in [inference.md](./inference.md)), you can proceed with the generation of lesion measures from test set predicted and ground truth lesions masks. We compute six different patient level lesion measures: patient-level lesion SUV<sub>mean</sub>, lesion SUV<sub>max</sub>, number of lesions, total metabolic tumor volume (TMTV) in ml, total lesion glycolysis (TLG) in ml, and lesion dissemination (D<sub>max</sub>) in cm. These metrics have been defined in [metrics/metrics.py](./../metrics/metrics.py) and have been shown to be prognostic biomarkers in lymphoma. 

## Step 1: Activate the required conda environment (`lymphoma_seg`) and navigate to `segmentation` folder

First, activate the conda environment `lymphoma_seg` using (created as described in [conda_env.md](./conda_env.md)):  

```

conda activate lymphoma_seg

cd segmentation

```

## Step 2: Run the script to compute test metrics

After this, run the following script in your terminal,

```

python generate_lesion_measures.py --fold=0 --network-name='unet' --input-patch-size=192

```

Alternatively, modify the [segmentation/generate_lesion_measures.sh](./../segmentation/generate_lesion_measures.sh) for your use-case (which contains the same bash script as above) and run:

```

bash generate_lesion_measures.sh

```

The ground truth and predicted lesion measures on the test set will be written to `LYMPHOMA_SEGMENTATION_FOLDER/results/test_lesion_measures/fold{fold}/{network_name}/{experiment_code}/testlesionmeasures.csv`, as described in [results_format.md](./results_format.md) file. The relevant directory structure may then look like:

    └───lymphoma.segmentation/

            ├── data

            └── results

                ├── logs

                ├── models

                ├── predictions

                └── test_metrics

                └── test_lesion_measures

                    ├── fold0

                    │   └── unet

                    │       └── unet_fold0_randcrop192

                    │           └── testlesionmeasures.csv   

                    └── fold1

                        └── unet

                            └── unet_fold1_randcrop192

                                └── testlesionmeasures.csv