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import os
import pydicom
import nibabel as nib
import pandas as pd
import dicom2nifti
from tqdm import tqdm
import concurrent.futures
def get_knee_side(dicom_path):
"""
Reads DICOM metadata and extracts the knee side from SeriesDescription.
Returns:
int: 1 for RIGHT, 2 for LEFT (based on SeriesDescription), or None on error.
"""
try:
ds = pydicom.dcmread(dicom_path, stop_before_pixels=True)
series_desc = getattr(ds, "SeriesDescription", "").upper()
print(series_desc)
return 'RIGHT' if series_desc == "SAG_3D_DESS_RIGHT" else 'LEFT'
except Exception as e:
print(f"Error reading DICOM file {dicom_path}: {e}")
return None
def convert_dcm_to_nifti(dicom_path, save_image_filepath):
"""
Converts DICOM files to NIfTI format.
Args:
dicom_path (str): Path to the DICOM file.
save_image_filepath (str): Path to save the NIfTI file.
"""
dicom2nifti.dicom_series_to_nifti(dicom_path, save_image_filepath, reorient_nifti=True)
print(f"Saved {save_image_filepath}")
return save_image_filepath
def process_row(row, save_image_folder):
"""
Process a single row from the DataFrame.
"""
patient_id = row['patient_id']
time = row['time']
study_folder = row['study_folder']
series_folder = row['series_folder']
knee_side = row['knee_side']
path = row['path']
save_image_filepath = os.path.join(save_image_folder, str(patient_id), str(time),
f'{study_folder}_{series_folder}_{knee_side}.nii.gz')
if os.path.exists(save_image_filepath):
print(f"Skipping {save_image_filepath} because it already exists")
else:
os.makedirs(os.path.dirname(save_image_filepath), exist_ok=True)
convert_dcm_to_nifti(path, save_image_filepath)
row_copy = row.copy()
row_copy['save_image_filepath'] = save_image_filepath
return row_copy
if __name__ == "__main__":
save_image_folder = '/data/images'
df = pd.read_csv('/data/all_studies.csv')
# Define number of workers (threads)
max_workers = 8 # You can adjust this based on your CPU cores
save_rows = []
# Process in batches if memory is a concern
batch_size = 100
for i in range(0, len(df), batch_size):
batch_df = df.iloc[i:i+batch_size]
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [executor.submit(process_row, row, save_image_folder)
for _, row in batch_df.iterrows()]
for future in tqdm(concurrent.futures.as_completed(futures),
total=len(futures),
desc=f"Batch {i//batch_size+1}/{(len(df)+batch_size-1)//batch_size}"):
try:
result = future.result()
save_rows.append(result)
except Exception as e:
print(f"Processing error: {e}")
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