Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files Community
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271171584/1045162547 [======>.......................] - ETA: 2:56
Loading data and preprocessing
The files are provided in Nifti format with the extension .nii. To read the scans, we use the nibabel package. You can install the package via pip install nibabel. CT scans store raw voxel intensity in Hounsfield units (HU). They range from -1024 to above 2000 in this dataset. Above 400 are bones with different radiointensity, so this is used as a higher bound. A threshold between -1000 and 400 is commonly used to normalize CT scans.
To process the data, we do the following:
We first rotate the volumes by 90 degrees, so the orientation is fixed
We scale the HU values to be between 0 and 1.
We resize width, height and depth.
Here we define several helper functions to process the data. These functions will be used when building training and validation datasets.
import nibabel as nib
from scipy import ndimage
def read_nifti_file(filepath):
\"\"\"Read and load volume\"\"\"
# Read file
scan = nib.load(filepath)
# Get raw data
scan = scan.get_fdata()
return scan
def normalize(volume):
\"\"\"Normalize the volume\"\"\"
min = -1000
max = 400
volume[volume < min] = min
volume[volume > max] = max
volume = (volume - min) / (max - min)
volume = volume.astype(\"float32\")
return volume
def resize_volume(img):
\"\"\"Resize across z-axis\"\"\"
# Set the desired depth
desired_depth = 64
desired_width = 128
desired_height = 128
# Get current depth
current_depth = img.shape[-1]
current_width = img.shape[0]
current_height = img.shape[1]
# Compute depth factor
depth = current_depth / desired_depth
width = current_width / desired_width
height = current_height / desired_height
depth_factor = 1 / depth
width_factor = 1 / width
height_factor = 1 / height
# Rotate
img = ndimage.rotate(img, 90, reshape=False)
# Resize across z-axis
img = ndimage.zoom(img, (width_factor, height_factor, depth_factor), order=1)
return img
def process_scan(path):
\"\"\"Read and resize volume\"\"\"
# Read scan
volume = read_nifti_file(path)
# Normalize
volume = normalize(volume)
# Resize width, height and depth
volume = resize_volume(volume)
return volume
Let's read the paths of the CT scans from the class directories.
# Folder \"CT-0\" consist of CT scans having normal lung tissue,
# no CT-signs of viral pneumonia.
normal_scan_paths = [
os.path.join(os.getcwd(), \"MosMedData/CT-0\", x)
for x in os.listdir(\"MosMedData/CT-0\")
]
# Folder \"CT-23\" consist of CT scans having several ground-glass opacifications,
# involvement of lung parenchyma.
abnormal_scan_paths = [
os.path.join(os.getcwd(), \"MosMedData/CT-23\", x)
for x in os.listdir(\"MosMedData/CT-23\")
]
print(\"CT scans with normal lung tissue: \" + str(len(normal_scan_paths)))
print(\"CT scans with abnormal lung tissue: \" + str(len(abnormal_scan_paths)))
CT scans with normal lung tissue: 100
CT scans with abnormal lung tissue: 100
Build train and validation datasets
Read the scans from the class directories and assign labels. Downsample the scans to have shape of 128x128x64. Rescale the raw HU values to the range 0 to 1. Lastly, split the dataset into train and validation subsets.
# Read and process the scans.
# Each scan is resized across height, width, and depth and rescaled.
abnormal_scans = np.array([process_scan(path) for path in abnormal_scan_paths])
normal_scans = np.array([process_scan(path) for path in normal_scan_paths])
# For the CT scans having presence of viral pneumonia
# assign 1, for the normal ones assign 0.
abnormal_labels = np.array([1 for _ in range(len(abnormal_scans))])
normal_labels = np.array([0 for _ in range(len(normal_scans))])