still script

Signed-off-by: neuronflow <florian.kofler@tum.de>

Author: neuronflow

preprocessing/example_modality_centric_preprocessor.py

@@ -0,0 +1,149 @@
+# TODO move this to tutorial
+# TODO polish this
+import datetime
+
+from auxiliary.normalization.percentile_normalizer import PercentileNormalizer
+from auxiliary.turbopath import turbopath
+from tqdm import tqdm
+
+from brainles_preprocessing.brain_extraction import HDBetExtractor
+from brainles_preprocessing.modality import Modality
+from brainles_preprocessing.preprocessor import Preprocessor
+from brainles_preprocessing.registration import NiftyRegRegistrator
+
+
+def preprocess(inputDir):
+    inputDir = turbopath(inputDir)
+    try:
+        print("*** start ***")
+
+        # where are the raw mr files?
+        btk_raw_dir = turbopath(inputDir)
+
+        # is the exam already processed?
+        brainles_dir = turbopath(inputDir) + "/" + inputDir.name + "_brainles"
+        raw_bet_dir = brainles_dir / "raw_bet"
+        norm_bet_dir = brainles_dir / "normalized_bet"
+        raw_skull_dir = brainles_dir / "raw_skull"
+        norm_skull_dir = brainles_dir / "normalized_skull"
+
+        # if not os.path.exists(prep_dir):
+        # if os.path.exists(prep_dir):
+        t1_file = btk_raw_dir.files("*t1.nii.gz")
+        t1c_file = btk_raw_dir.files("*t1c.nii.gz")
+        t2_file = btk_raw_dir.files("*t2.nii.gz")
+        flair_file = btk_raw_dir.files("*fla.nii.gz")
+
+        if len(t1_file) == len(t1c_file) == len(t2_file) == len(flair_file) == 1:
+            # print(t1_file)
+            # print(t1c_file)
+            # print(t2_file)
+            # print(flair_file)
+
+            t1File = t1_file[0]
+            t1cFile = t1c_file[0]
+            t2File = t2_file[0]
+            flaFile = flair_file[0]
+
+            # normalizer
+            percentile_normalizer = PercentileNormalizer(
+                lower_percentile=0.1,
+                upper_percentile=99.9,
+                lower_limit=0,
+                upper_limit=1,
+            )
+
+            # define modalities
+            center = Modality(
+                modality_name="t1c",
+                input_path=t1cFile,
+                raw_bet_output_path=raw_bet_dir / inputDir.name + "_t1c_bet_raw.nii.gz",
+                raw_skull_output_path=raw_skull_dir / inputDir.name
+                + "_t1c_skull_raw.nii.gz",
+                normalized_bet_output_path=norm_bet_dir / inputDir.name
+                + "_t1c_bet_normalized.nii.gz",
+                normalized_skull_output_path=norm_skull_dir / inputDir.name
+                + "_t1c_skull_normalized.nii.gz",
+                atlas_correction=True,
+                normalizer=percentile_normalizer,
+            )
+
+            moving_modalities = [
+                Modality(
+                    modality_name="t1",
+                    input_path=t1File,
+                    raw_bet_output_path=raw_bet_dir / inputDir.name
+                    + "_t1_bet_raw.nii.gz",
+                    raw_skull_output_path=raw_skull_dir / inputDir.name
+                    + "_t1_skull_raw.nii.gz",
+                    normalized_bet_output_path=norm_bet_dir / inputDir.name
+                    + "_t1_bet_normalized.nii.gz",
+                    normalized_skull_output_path=norm_skull_dir / inputDir.name
+                    + "_t1_skull_normalized.nii.gz",
+                    atlas_correction=True,
+                    normalizer=percentile_normalizer,
+                ),
+                Modality(
+                    modality_name="t2",
+                    input_path=t2File,
+                    raw_bet_output_path=raw_bet_dir / inputDir.name
+                    + "_t2_bet_raw.nii.gz",
+                    raw_skull_output_path=raw_skull_dir / inputDir.name
+                    + "_t2_skull_raw.nii.gz",
+                    normalized_bet_output_path=norm_bet_dir / inputDir.name
+                    + "_t2_bet_normalized.nii.gz",
+                    normalized_skull_output_path=norm_skull_dir / inputDir.name
+                    + "_t2_skull_normalized.nii.gz",
+                    atlas_correction=True,
+                    normalizer=percentile_normalizer,
+                ),
+                Modality(
+                    modality_name="flair",
+                    input_path=flaFile,
+                    raw_bet_output_path=raw_bet_dir / inputDir.name
+                    + "_fla_bet_raw.nii.gz",
+                    raw_skull_output_path=raw_skull_dir / inputDir.name
+                    + "_fla_skull_raw.nii.gz",
+                    normalized_bet_output_path=norm_bet_dir / inputDir.name
+                    + "_fla_bet_normalized.nii.gz",
+                    normalized_skull_output_path=norm_skull_dir / inputDir.name
+                    + "_fla_skull_normalized.nii.gz",
+                    atlas_correction=True,
+                    normalizer=percentile_normalizer,
+                ),
+            ]
+
+            preprocessor = Preprocessor(
+                center_modality=center,
+                moving_modalities=moving_modalities,
+                registrator=NiftyRegRegistrator(),
+                brain_extractor=HDBetExtractor(),
+                temp_folder="tempo",
+                limit_cuda_visible_devices="1",
+            )
+
+            preprocessor.run(
+                save_dir_coregistration=brainles_dir + "/co-registration",
+                save_dir_atlas_registration=brainles_dir + "/atlas-registration",
+                save_dir_atlas_correction=brainles_dir + "/atlas-correction",
+                save_dir_brain_extraction=brainles_dir + "/brain-extraction",
+            )
+
+    except Exception as e:
+        print("error: " + str(e))
+        print("conversion error for:", inputDir)
+
+        time = str(datetime.datetime.now().time())
+
+        print("** finished:", inputDir.name, "at:", time)
+
+
+### *** GOGOGO *** ###
+if __name__ == "__main__":
+    EXAMPLE_DATA_DIR = turbopath("example/example_data")
+
+    exams = EXAMPLE_DATA_DIR.dirs()
+
+    for exam in tqdm(exams):
+        print("processing:", exam)
+        preprocess(exam)