udpate phase prediction model

CHANGELOG.md

@@ -1,6 +1,7 @@
 ## Master
 * add brain structures
 * add liver vessels
+* greatly improved phase classification model
 
 
 ## Release 2.3.0

README.md

@@ -133,15 +133,6 @@ If you want to reduce memory consumption you can use the following options:
 * `--nr_thr_saving 1`: Saving big images with several threads will take a lot of memory
 
 
-### Train/validation/test split
-The exact split of the dataset can be found in the file `meta.csv` inside of the [dataset](https://doi.org/10.5281/zenodo.6802613). This was used for the validation in our paper.
-The exact numbers of the results for the high-resolution model (1.5mm) can be found [here](resources/results_all_classes_v1.json). The paper shows these numbers in the supplementary materials Figure 11.
-
-
-### Retrain model and run evaluation
-See [here](resources/train_nnunet.md) for more info on how to train a nnU-Net yourself on the TotalSegmentator dataset, how to split the data into train/validation/test set as in our paper, and how to run the same evaluation as in our paper.
-
-
 ### Python API
 You can run totalsegmentator via Python:
 ```python
@@ -159,13 +150,12 @@ if __name__ == "__main__":
 ```
 You can see all available arguments [here](https://github.com/wasserth/TotalSegmentator/blob/master/totalsegmentator/python_api.py). Running from within the main environment should avoid some multiprocessing issues.
 
-The segmentation image contains the names of the classes in the extended header. If you want to load this additional header information you can use the following code:
+The segmentation image contains the names of the classes in the extended header. If you want to load this additional header information you can use the following code (requires `pip install xmltodict`):
 ```python
 from totalsegmentator.nifti_ext_header import load_multilabel_nifti
 
 segmentation_nifti_img, label_map_dict = load_multilabel_nifti(image_path)
 ```
-The above code requires `pip install xmltodict`.
 
 
 ### Install latest master branch (contains latest bug fixes)
@@ -175,6 +165,11 @@ pip install git+https://github.com/wasserth/TotalSegmentator.git
 
 
 ### Other commands
+If you want to know which contrast phase a CT image is you can use the following command (requires `pip install xgboost`). More details can be found [here](resources/contrast_phase_prediction.md):
+```
+totalseg_get_phase -i ct.nii.gz -o contrast_phase.json
+```
+
 If you want to combine some subclasses (e.g. lung lobes) into one binary mask (e.g. entire lung) you can use the following command:
 ```
 totalseg_combine_masks -i totalsegmentator_output_dir -o combined_mask.nii.gz -m lungcomm 
@@ -191,6 +186,15 @@ totalseg_set_license -l aca_12345678910
 ```
 
 
+### Train/validation/test split
+The exact split of the dataset can be found in the file `meta.csv` inside of the [dataset](https://doi.org/10.5281/zenodo.6802613). This was used for the validation in our paper.
+The exact numbers of the results for the high-resolution model (1.5mm) can be found [here](resources/results_all_classes_v1.json). The paper shows these numbers in the supplementary materials Figure 11.
+
+
+### Retrain model and run evaluation
+See [here](resources/train_nnunet.md) for more info on how to train a nnU-Net yourself on the TotalSegmentator dataset, how to split the data into train/validation/test set as in our paper, and how to run the same evaluation as in our paper.
+
+
 ### Typical problems
 
 **ITK loading Error**

resources/contrast_phase_prediction.md

@@ -0,0 +1,30 @@
+# Details on how the prediction of the contrast phase is done
+
+TotalSegmentator is used to predict the following structures:
+```python
+["liver", "pancreas", "urinary_bladder", "gallbladder",
+"heart", "aorta", "inferior_vena_cava", "portal_vein_and_splenic_vein",
+"iliac_vena_left", "iliac_vena_right", "iliac_artery_left", "iliac_artery_right",
+"pulmonary_vein", "brain", "colon", "small_bowel",
+"internal_carotid_artery_right", "internal_carotid_artery_left",
+"internal_jugular_vein_right", "internal_jugular_vein_left"]
+```
+Then the median intensity (HU value) of each structure is used as feature for a xgboost classifier
+to predict the post injection time (pi_time). The pi_time can be mapped to the contrast phase
+then. It classifies into `native`, `arterial_early`, `arterial_late`, and `portal_venous` phase.
+The classifier was trained on the TotalSegmentator dataset and therefore works with all sorts 
+of different CT images.
+
+Results on 5-fold cross validation:
+
+- Mean absolute error (MAE): 5.55s
+- F1 scores for each class:
+  - native: 0.980
+  - arterial_early+late: 0.915
+  - portal: 0.940
+
+The results contain a probablity for each class which is high if the predicted pi_time is close to the ideal
+pi_time for the given phase. Moreover, the classifier is an ensemble of 5 models. The output contains the 
+standard deviation of the predictions which can be used as a measure of confidence. If it is low the 5 models
+give similar predictions which is a good sign.
+

setup.py

@@ -11,7 +11,10 @@
         python_requires='>=3.9',
         license='Apache 2.0',
         packages=find_packages(),
-        package_data={"totalsegmentator": ["resources/totalsegmentator_snomed_mapping.csv"]},
+        package_data={"totalsegmentator": 
+            ["resources/totalsegmentator_snomed_mapping.csv",
+             "resources/contrast_phase_classifiers_2024_07_19.pkl"]
+            },
         install_requires=[
             'torch>=2.0.0',
             'numpy<2',

totalsegmentator/bin/totalseg_get_phase.py

@@ -68,7 +68,7 @@ def get_ct_contrast_phase(ct_img: nib.Nifti1Image, model_file: Path = None):
     # print(f"ts took: {time.time()-st:.2f}s")
 
     if stats["brain"]["volume"] > 100:
-        # print(f"Brain in image, therefore also running headneck model.")
+        # print("Brain in image, therefore also running headneck model.")
         st = time.time()
         seg_img_hn, stats_hn = totalsegmentator(ct_img, None, ml=True, fast=False, statistics=True, 
                                                 task="headneck_bones_vessels",
@@ -85,11 +85,9 @@ def get_ct_contrast_phase(ct_img: nib.Nifti1Image, model_file: Path = None):
         features.append(stats_hn[organ]["intensity"])
 
     if model_file is None:
-        # weights from longitudinalliver dataset
-        classifier_path = Path(__file__).parents[2] / "resources" / "contrast_phase_classifiers.pkl"
-    else:
-        # weights from megaseg dataset
-        # classifier_path = "/mnt/nor/wasserthalj_data/classifiers_megaseg.pkl"
+        classifier_path = Path(__file__).parents[2] / "resources" / "contrast_phase_classifiers_2024_07_19.pkl"
+    else: 
+        # manually set model file
         classifier_path = model_file
     clfs = pickle.load(open(classifier_path, "rb"))
 
@@ -136,13 +134,17 @@ def main():
     parser.add_argument("-m", metavar="filepath", dest="model_file",
                         help="path to classifier model",
                         type=lambda p: Path(p).absolute(), required=False, default=None)
+
+    parser.add_argument("-q", dest="quiet", action="store_true",
+                        help="Print no output to stdout", default=False)
 
     args = parser.parse_args()
 
     res = get_ct_contrast_phase(nib.load(args.input_file), args.model_file)
 
-    print("Result:")
-    pprint(res)
+    if not args.quiet:
+        print("Result:")
+        pprint(res)
 
     with open(args.output_file, "w") as f:
         f.write(json.dumps(res, indent=4))