Spaces:
Runtime error
Runtime error
File size: 3,725 Bytes
caf6ee7 906fcb9 caf6ee7 906fcb9 caf6ee7 906fcb9 1baebae caf6ee7 1baebae 906fcb9 1baebae 906fcb9 1baebae 906fcb9 1baebae 906fcb9 caf6ee7 906fcb9 1baebae 906fcb9 1baebae 906fcb9 1baebae 906fcb9 1baebae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import argparse
import logging
import os
import SimpleITK as sitk
from picai_prep.preprocessing import PreprocessingSettings, Sample
from tqdm import tqdm
from .center_crop import crop
def register_files(args: argparse.Namespace) -> argparse.Namespace:
"""
Register and crop medical images (T2, DWI, and ADC) to a standardized spacing and size.
This function reads medical images from specified directories, resamples them to a
new spacing of (0.4, 0.4, 3.0) mm, preprocesses them using the Sample class, and crops
them with specified margins. The processed images are saved to new output directories.
Args:
args: An argument object containing:
- t2_dir (str): Directory path containing T2 weighted images
- dwi_dir (str): Directory path containing DWI (Diffusion Weighted Imaging) images
- adc_dir (str): Directory path containing ADC (Apparent Diffusion Coefficient) images
- output_dir (str): Directory path where registered images will be saved
- margin (float): Margin in mm to crop from x and y dimensions
Returns:
args: Updated argument object with modified directory paths pointing to the
registered image directories (t2_registered, DWI_registered, ADC_registered)
Raises:
FileNotFoundError: If input directories do not exist or files cannot be read
RuntimeError: If image preprocessing or cropping fails
"""
files = os.listdir(args.t2_dir)
new_spacing = (0.4, 0.4, 3.0)
t2_registered_dir = os.path.join(args.output_dir, "t2_registered")
dwi_registered_dir = os.path.join(args.output_dir, "DWI_registered")
adc_registered_dir = os.path.join(args.output_dir, "ADC_registered")
os.makedirs(t2_registered_dir, exist_ok=True)
os.makedirs(dwi_registered_dir, exist_ok=True)
os.makedirs(adc_registered_dir, exist_ok=True)
logging.info("Starting registration and cropping")
for file in tqdm(files):
t2_image = sitk.ReadImage(os.path.join(args.t2_dir, file))
dwi_image = sitk.ReadImage(os.path.join(args.dwi_dir, file))
adc_image = sitk.ReadImage(os.path.join(args.adc_dir, file))
original_spacing = t2_image.GetSpacing()
original_size = t2_image.GetSize()
new_size = [
int(round(osz * ospc / nspc))
for osz, ospc, nspc in zip(original_size, original_spacing, new_spacing)
]
images_to_preprocess = {}
images_to_preprocess["t2"] = t2_image
images_to_preprocess["hbv"] = dwi_image
images_to_preprocess["adc"] = adc_image
pat_case = Sample(
scans=[
images_to_preprocess["t2"],
images_to_preprocess["hbv"],
images_to_preprocess["adc"],
],
settings=PreprocessingSettings(
spacing=[3.0, 0.4, 0.4], matrix_size=[new_size[2], new_size[1], new_size[0]]
),
)
pat_case.preprocess()
t2_post = pat_case.__dict__["scans"][0]
dwi_post = pat_case.__dict__["scans"][1]
adc_post = pat_case.__dict__["scans"][2]
cropped_t2 = crop(t2_post, [args.margin, args.margin, 0.0])
cropped_dwi = crop(dwi_post, [args.margin, args.margin, 0.0])
cropped_adc = crop(adc_post, [args.margin, args.margin, 0.0])
sitk.WriteImage(cropped_t2, os.path.join(t2_registered_dir, file))
sitk.WriteImage(cropped_dwi, os.path.join(dwi_registered_dir, file))
sitk.WriteImage(cropped_adc, os.path.join(adc_registered_dir, file))
args.t2_dir = t2_registered_dir
args.dwi_dir = dwi_registered_dir
args.adc_dir = adc_registered_dir
return args
|