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1009e1f | 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | import cv2
import glob
import pydicom
import numpy as np
import pandas as pd
from collections import Counter
class dotdict(dict):
"""
A dictionary that allows accessing its elements as attributes.
"""
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def __getattr__(self, name):
"""
Get an attribute from the dictionary.
Args:
name (str): The name of the attribute to get.
Returns:
The value of the attribute.
Raises:
AttributeError: If the attribute does not exist.
"""
try:
return self[name]
except KeyError:
raise AttributeError(name)
def read_series_metadata(
study_id,
series_id,
series_description,
data_path="../input/train_images/",
return_imgs=True,
):
"""
Reads the metadata and images from a DICOM series.
Args:
study_id (int): The ID of the study.
series_id (int): The ID of the series.
series_description (str): The description of the series.
data_path (str, optional): The path to the input data. Defaults to "../input/train_images/".
return_imgs (bool, optional): Whether to return the images. Defaults to True.
Returns:
tuple: A tuple containing the DataFrame with metadata and the list of images.
"""
dicom_dir = data_path + f"{study_id}/{series_id}"
# read dicom file
dicom_file = sorted(
glob.glob(f"{dicom_dir}/*.dcm"), key=lambda x: int(x.split("/")[-1][:-4])
)
instance_number = [int(f.split("/")[-1].split(".")[0]) for f in dicom_file]
dicom = [pydicom.dcmread(f, stop_before_pixels=False) for f in dicom_file]
dicom_df, images = [], {}
for i, d in zip(instance_number, dicom): # d__.dict__
images[i] = d.pixel_array if return_imgs else None
try:
dicom_df.append(
dotdict(
study_id=study_id,
series_id=series_id,
series_description=series_description,
instance_number=i,
ImagePositionPatient=[float(v) for v in d.ImagePositionPatient],
ImageOrientationPatient=[
float(v) for v in d.ImageOrientationPatient
],
PixelSpacing=[float(v) for v in d.PixelSpacing],
SpacingBetweenSlices=float(d.SpacingBetweenSlices),
SliceThickness=float(d.SliceThickness),
grouping=0,
)
)
except Exception: # Missing fields
dicom_df.append(
dotdict(
study_id=study_id,
series_id=series_id,
series_description=series_description,
instance_number=i,
ImagePositionPatient=[float(v) for v in d.ImagePositionPatient],
ImageOrientationPatient=-1,
PixelSpacing=-1,
SpacingBetweenSlices=-1,
SliceThickness=-1,
grouping=0,
)
)
dicom_df = pd.DataFrame(dicom_df)
# Sort slices
# More robust orientation check based on ImageOrientationPatient if description fails
if "sagittal" in series_description.lower():
dicom_df["order"] = dicom_df["ImagePositionPatient"].apply(lambda x: x[0])
elif "axial" in series_description.lower():
dicom_df["order"] = dicom_df["ImagePositionPatient"].apply(lambda x: x[2])
else:
# Fallback using ImageOrientationPatient
# Assuming ImageOrientationPatient is [r_x, r_y, r_z, c_x, c_y, c_z]
# Cross product gives the normal vector (slice direction)
orientations = dicom_df["ImageOrientationPatient"].values
orders = []
for i, pos in enumerate(dicom_df["ImagePositionPatient"].values):
if type(orientations[i]) is list and len(orientations[i]) == 6:
r = np.array(orientations[i][:3])
c = np.array(orientations[i][3:])
normal = np.cross(r, c)
# the position along the normal vector
orders.append(np.dot(normal, pos))
else:
orders.append(pos[0]) # naive fallback
dicom_df["order"] = orders
df = dicom_df.sort_values("order", ignore_index=True)
df["group"] = 0
df.loc[:, "z"] = np.arange(len(df))
images = [images[k] for k in df["instance_number"]]
return df.reset_index(drop=True), images
def process_2(study, series, orient, data_path="", on_gpu=False):
"""
Processes the DICOM series and returns the images and metadata.
Args:
study (int): The ID of the study.
series (int): The ID of the series.
orient (str): The orientation of the series.
data_path (str, optional): The path to the input data directory. Defaults to "".
on_gpu (bool, optional): Whether to process on GPU. Defaults to False.
Returns:
tuple: A tuple containing the processed images and the DataFrame with metadata.
"""
df, imgs = read_series_metadata(
study,
series,
orient,
data_path=data_path,
)
try:
imgs = np.array(imgs)
except Exception:
shapes = Counter([img.shape for img in imgs])
shape = shapes.most_common()[0][0]
# print("Different shapes:", shapes, f"resize to {shape} - {orient}")
imgs = np.array(
[cv2.resize(img, shape) if img.shape != shape else img for img in imgs]
)
return imgs, df
def process_and_save(
study,
series,
orient,
data_path,
save_folder="",
save_meta=False,
save_middle_frame=False,
):
"""
Processes the DICOM series and saves the images and metadata.
Args:
study (int): The ID of the study.
series (int): The ID of the series.
orient (str): The orientation of the series.
data_path (str): The path to the input data directory.
save_folder (str, optional): Path to the saving folder. Defaults to "".
save_meta (bool, optional): Whether to save the metadata. Defaults to False.
save_middle_frame (bool, optional): Whether to save the middle frame. Defaults to False.
Returns:
dict: A dictionary containing the study ID, series ID, and the list of frame numbers.
"""
imgs, df_series = process_2(int(study), int(series), orient, data_path=data_path)
if save_folder:
np.save(save_folder + f"npy/{study}_{series}.npy", imgs)
if save_meta:
df_series.to_csv(save_folder + f"csv/{study}_{series}.csv", index=False)
if save_middle_frame:
img = imgs[len(imgs) // 2]
img = np.clip(
img, np.percentile(img.flatten(), 0), np.percentile(img.flatten(), 98)
)
max_, min_ = img.max(), img.min()
if max_ != min_:
img = (img - min_) / (max_ - min_)
else:
img = img - min_
img = (img * 255).astype(np.uint8)
cv2.imwrite(save_folder + f"mid/{study}_{series}.png", img)
return {
"study_id": study,
"series_id": series,
"frames": df_series.instance_number.values.tolist(),
}
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