diff --git a/.gitignore b/.gitignore index a5c40b783..e9c99a8ae 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ store/ unittest_prediction unittest_prediction_fast unittest_prediction_roi_subset +unittest_prediction_fast.nii.gz *.tfstate *.tfstate.backup diff --git a/README.md b/README.md index 0948023cb..2d16971e2 100644 --- a/README.md +++ b/README.md @@ -60,11 +60,8 @@ runtime. ![Alt text](resources/imgs/overview_subclasses.png) We added some more models to TotalSegmentator beyond the default one. This allows segmentation of even -more classes in more detailed subparts of the image. First you have to run TotalSegmentator with the -normal settings to get the normal masks. These masks are required to crop the image to a subregion on -which the detailed model will run. +more classes in more detailed subparts of the image. ``` -TotalSegmentator -i ct.nii.gz -o segmentations --fast TotalSegmentator -i ct.nii.gz -o segmentations -ta lung_vessels ``` Overview of available subtasks and the classes which they contain. diff --git a/tests.sh b/tests.sh index 6ad805520..fd5e319c8 100755 --- a/tests.sh +++ b/tests.sh @@ -1,35 +1,41 @@ set -e -echo "License key: $1" +# To run these tests simply do +# ./tests.sh -# # Test organ predictions -# TotalSegmentator -i tests/reference_files/example_ct.nii.gz -o tests/unittest_prediction --test 1 -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_liver -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_vertebrae -# # Test multilabel prediction -# TotalSegmentator -i tests/reference_files/example_ct.nii.gz -o tests/unittest_prediction.nii.gz --ml --test 1 -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_multilabel +# Test multilabel prediction +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction.nii.gz -bs --ml +pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_multilabel # Test organ prediction - roi subset -# 20 cpu -# example_ct_sm.nii.gz: 20s, 3.1GB -# example_ct.nii.gz: 22s, 3.1GB # 2 cpus: # example_ct_sm.nii.gz: 34s, 3.0GB # example_ct.nii.gz: 36s, 3.0GB TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction_roi_subset -rs liver brain -d cpu pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_liver_roi_subset -# Test organ predictions - fast -# 2 cpus: (w/o statistics) +# Test organ predictions - fast - statistics +# 2 cpus: (statistics <1s) # example_ct_sm.nii.gz: 13s, 4.1GB # example_ct.nii.gz: 16s, 4.1GB -# TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction_fast --fast --statistics -d cpu -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_liver_fast -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_vertebrae_fast -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_statistics - -# # Test vessel predictions -# TotalSegmentator -i tests/reference_files/example_ct.nii.gz -o tests/unittest_prediction -ta lung_vessels --test 3 -d cpu -# pytest -v tests/test_end_to_end.py::test_end_to_end::test_lung_vessels \ No newline at end of file +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction_fast --fast --statistics -d cpu +pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_fast +pytest -v tests/test_end_to_end.py::test_end_to_end::test_statistics +# pytest -v tests/test_end_to_end.py::test_end_to_end::test_preview + +# Test organ predictions - fast - multilabel +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction_fast.nii.gz --fast --ml -d cpu +pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_multilabel_fast + +# Test organ predictions - fast - multilabel - body_seg +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction_fast_body_seg.nii.gz --fast --ml -bs -d cpu +pytest -v tests/test_end_to_end.py::test_end_to_end::test_prediction_multilabel_fast_body_seg + +# Test vessel predictions +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction -ta lung_vessels -d cpu # ~1min +pytest -v tests/test_end_to_end.py::test_end_to_end::test_lung_vessels + +# Test tissue types (with license) + body_seg +TotalSegmentator -i tests/reference_files/example_ct_sm.nii.gz -o tests/unittest_prediction -ta tissue_types -d cpu -l $1 +pytest -v tests/test_end_to_end.py::test_end_to_end::test_tissue_types diff --git a/tests/reference_files/example_seg.nii.gz b/tests/reference_files/example_seg.nii.gz 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b/tests/reference_files/example_seg_fast/small_bowel.nii.gz differ diff --git a/tests/reference_files/example_seg_fast/spinal_cord.nii.gz b/tests/reference_files/example_seg_fast/spinal_cord.nii.gz new file mode 100644 index 000000000..6f824f51f Binary files /dev/null and b/tests/reference_files/example_seg_fast/spinal_cord.nii.gz differ diff --git a/tests/reference_files/example_seg_fast/spleen.nii.gz b/tests/reference_files/example_seg_fast/spleen.nii.gz index bff20e77e..48532ac42 100644 Binary files a/tests/reference_files/example_seg_fast/spleen.nii.gz and b/tests/reference_files/example_seg_fast/spleen.nii.gz differ diff --git a/tests/reference_files/example_seg_fast/statistics.json b/tests/reference_files/example_seg_fast/statistics.json index 920aa16ea..09a924d7e 100644 --- a/tests/reference_files/example_seg_fast/statistics.json +++ b/tests/reference_files/example_seg_fast/statistics.json @@ -1,99 +1,135 @@ { "spleen": { - "volume": 260118.0, - "intensity": 32.4 + "volume": 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b/tests/reference_files/example_seg_tissue_types/subcutaneous_fat.nii.gz new file mode 100644 index 000000000..daeddba10 Binary files /dev/null and b/tests/reference_files/example_seg_tissue_types/subcutaneous_fat.nii.gz differ diff --git a/tests/reference_files/example_seg_tissue_types/torso_fat.nii.gz b/tests/reference_files/example_seg_tissue_types/torso_fat.nii.gz new file mode 100644 index 000000000..23c7bbf5e Binary files /dev/null and b/tests/reference_files/example_seg_tissue_types/torso_fat.nii.gz differ diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py index 445ec9f44..168d4532a 100644 --- a/tests/test_end_to_end.py +++ b/tests/test_end_to_end.py @@ -1,3 +1,4 @@ +import os import unittest import json import nibabel as nib @@ -10,18 +11,6 @@ class test_end_to_end(unittest.TestCase): def setUp(self): pass - def test_prediction_liver(self): - img_ref = nib.load("tests/reference_files/example_seg/liver.nii.gz").get_fdata() - img_new = nib.load("tests/unittest_prediction/liver.nii.gz").get_fdata() - images_equal = np.array_equal(img_ref, img_new) - self.assertTrue(images_equal, "liver prediction not correct") - - def test_prediction_vertebrae(self): - img_ref = nib.load("tests/reference_files/example_seg/vertebrae_L1.nii.gz").get_fdata() - img_new = nib.load("tests/unittest_prediction/vertebrae_L1.nii.gz").get_fdata() - images_equal = np.array_equal(img_ref, img_new) - self.assertTrue(images_equal, "vertebrae prediction not correct") - def test_prediction_liver_roi_subset(self): for roi in ["liver", "brain"]: img_ref = nib.load(f"tests/reference_files/example_seg_roi_subset/{roi}.nii.gz").get_fdata() @@ -31,20 +20,18 @@ def test_prediction_liver_roi_subset(self): images_equal = nr_of_diff_voxels < 20 self.assertTrue(images_equal, f"{roi} roi subset prediction not correct (nr_of_diff_voxels: {nr_of_diff_voxels})") - def test_prediction_liver_fast(self): - img_ref = nib.load("tests/reference_files/example_seg_fast_cpu/liver.nii.gz").get_fdata() - img_new = nib.load("tests/unittest_prediction_fast/liver.nii.gz").get_fdata() - # prediction is not completely deterministic therefore allow for small differences - nr_of_diff_voxels = (img_ref != img_new).sum() - images_equal = nr_of_diff_voxels < 20 - self.assertTrue(images_equal, f"liver fast prediction not correct (nr_of_diff_voxels: {nr_of_diff_voxels})") + def test_prediction_fast(self): + for roi in ["liver", "vertebrae_L1"]: + img_ref = nib.load(f"tests/reference_files/example_seg_fast/{roi}.nii.gz").get_fdata() + img_new = nib.load(f"tests/unittest_prediction_fast/{roi}.nii.gz").get_fdata() + # prediction is not completely deterministic therefore allow for small differences + nr_of_diff_voxels = (img_ref != img_new).sum() + images_equal = nr_of_diff_voxels < 20 + self.assertTrue(images_equal, f"{roi} fast prediction not correct (nr_of_diff_voxels: {nr_of_diff_voxels})") - def test_prediction_vertebrae_fast(self): - img_ref = nib.load("tests/reference_files/example_seg_fast_cpu/vertebrae_L1.nii.gz").get_fdata() - img_new = nib.load("tests/unittest_prediction_fast/vertebrae_L1.nii.gz").get_fdata() - nr_of_diff_voxels = (img_ref != img_new).sum() - images_equal = nr_of_diff_voxels < 20 - self.assertTrue(images_equal, "vertebrae fast prediction not correct (nr_of_diff_voxels: {nr_of_diff_voxels})") + def test_preview(self): + preview_exists = os.path.exists(f"tests/reference_files/example_seg_fast/preview_total.png") + self.assertTrue(preview_exists, f"Preview was not generated") def test_prediction_multilabel(self): img_ref = nib.load("tests/reference_files/example_seg.nii.gz").get_fdata() @@ -52,14 +39,34 @@ def test_prediction_multilabel(self): images_equal = np.array_equal(img_ref, img_new) self.assertTrue(images_equal, "multilabel prediction not correct") - def test_lung_vessels(self): - img_ref = nib.load("tests/reference_files/example_seg/lung_vessels.nii.gz").get_fdata() - img_new = nib.load("tests/unittest_prediction/lung_vessels.nii.gz").get_fdata() + def test_prediction_multilabel_fast(self): + img_ref = nib.load("tests/reference_files/example_seg_fast.nii.gz").get_fdata() + img_new = nib.load("tests/unittest_prediction_fast.nii.gz").get_fdata() images_equal = np.array_equal(img_ref, img_new) - self.assertTrue(images_equal, "lung_vessel prediction not correct") + self.assertTrue(images_equal, "multilabel prediction fast not correct") + + def test_prediction_multilabel_fast_body_seg(self): + img_ref = nib.load("tests/reference_files/example_seg_fast_body_seg.nii.gz").get_fdata() + img_new = nib.load("tests/unittest_prediction_fast_body_seg.nii.gz").get_fdata() + images_equal = np.array_equal(img_ref, img_new) + self.assertTrue(images_equal, "body_seg prediction fast not correct") + + def test_lung_vessels(self): + for roi in ["lung_trachea_bronchia", "lung_vessels"]: + img_ref = nib.load(f"tests/reference_files/example_seg_lung_vessels/{roi}.nii.gz").get_fdata() + img_new = nib.load(f"tests/unittest_prediction/{roi}.nii.gz").get_fdata() + images_equal = np.array_equal(img_ref, img_new) + self.assertTrue(images_equal, f"{roi} prediction not correct") + + def test_tissue_types(self): + for roi in ["subcutaneous_fat", "skeletal_muscle", "torso_fat"]: + img_ref = nib.load(f"tests/reference_files/example_seg_tissue_types/{roi}.nii.gz").get_fdata() + img_new = nib.load(f"tests/unittest_prediction/{roi}.nii.gz").get_fdata() + images_equal = np.array_equal(img_ref, img_new) + self.assertTrue(images_equal, f"{roi} prediction not correct") def test_statistics(self): - stats_ref = json.load(open("tests/reference_files/example_seg_fast_cpu/statistics.json", "r")) + stats_ref = json.load(open("tests/reference_files/example_seg_fast/statistics.json", "r")) stats_ref = pd.DataFrame(stats_ref) stats_new = json.load(open("tests/unittest_prediction_fast/statistics.json", "r")) stats_new = pd.DataFrame(stats_new) @@ -70,17 +77,17 @@ def test_statistics(self): rtol=3e-2, atol=3e-2) self.assertTrue(stats_equal, "intensity statistics are not correct") - def test_radiomics(self): - stats_ref = json.load(open("tests/reference_files/example_seg_fast_cpu/statistics_radiomics.json", "r")) - stats_ref = pd.DataFrame(stats_ref) - stats_ref = stats_ref.fillna(0) - stats_new = json.load(open("tests/unittest_prediction_fast/statistics_radiomics.json", "r")) - stats_new = pd.DataFrame(stats_new) - stats_new = stats_new.fillna(0) - # very big margin, but some of the radiomics features can change a lot if only a few voxels - # of the segmentation change. So this test is only to check that radiomics ran sucessfully. - stats_equal = np.allclose(stats_ref.values, stats_new.values, rtol=3e-1, atol=3e-1) - self.assertTrue(stats_equal, "radiomics is not correct") + # def test_radiomics(self): + # stats_ref = json.load(open("tests/reference_files/example_seg_fast/statistics_radiomics.json", "r")) + # stats_ref = pd.DataFrame(stats_ref) + # stats_ref = stats_ref.fillna(0) + # stats_new = json.load(open("tests/unittest_prediction_fast/statistics_radiomics.json", "r")) + # stats_new = pd.DataFrame(stats_new) + # stats_new = stats_new.fillna(0) + # # very big margin, but some of the radiomics features can change a lot if only a few voxels + # # of the segmentation change. So this test is only to check that radiomics ran sucessfully. + # stats_equal = np.allclose(stats_ref.values, stats_new.values, rtol=3e-1, atol=3e-1) + # self.assertTrue(stats_equal, "radiomics is not correct") if __name__ == '__main__': diff --git a/tests/unittest_prediction.nii.gz b/tests/unittest_prediction.nii.gz new file mode 100644 index 000000000..1f518a4b5 Binary files /dev/null and b/tests/unittest_prediction.nii.gz differ diff --git a/tests/unittest_prediction_fast_body_seg.nii.gz b/tests/unittest_prediction_fast_body_seg.nii.gz new file mode 100644 index 000000000..c8ae81e0c Binary files /dev/null and b/tests/unittest_prediction_fast_body_seg.nii.gz differ diff --git a/totalsegmentator/nnunet.py b/totalsegmentator/nnunet.py index 99ab25c15..57e8cd535 100644 --- a/totalsegmentator/nnunet.py +++ b/totalsegmentator/nnunet.py @@ -492,8 +492,10 @@ def nnUNet_predict_image(file_in, file_out, task_id, model="3d_fullres", folds=N if not quiet: print(f" Saved in {time.time() - st:.2f}s") # Postprocessing - if task_name == "lung_vessels": - remove_outside_of_mask(file_out / "lung_vessels.nii.gz", file_out / "lung.nii.gz") + + # Lung mask does not exist since I use 6mm model. Would have to save lung mask from 6mm seg. + # if task_name == "lung_vessels": + # remove_outside_of_mask(file_out / "lung_vessels.nii.gz", file_out / "lung.nii.gz") # if task_name == "heartchambers_test": # remove_outside_of_mask(file_out / "heart_myocardium.nii.gz", file_out / "heart.nii.gz", addon=5)