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pre_CT_MR.py
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pre_CT_MR.py
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# -*- coding: utf-8 -*-
# %% import packages
# pip install connected-components-3d
import numpy as np
# import nibabel as nib
import SimpleITK as sitk
import os
join = os.path.join
from skimage import transform
from tqdm import tqdm
import cc3d
# convert nii image to npz files, including original image and corresponding masks
modality = "CT"
anatomy = "Abd" # anantomy + dataset name
img_name_suffix = "_0000.nii.gz"
gt_name_suffix = ".nii.gz"
prefix = modality + "_" + anatomy + "_"
nii_path = "data/FLARE22Train/images" # path to the nii images
gt_path = "data/FLARE22Train/labels" # path to the ground truth
npy_path = "data/npy/" + prefix[:-1]
os.makedirs(join(npy_path, "gts"), exist_ok=True)
os.makedirs(join(npy_path, "imgs"), exist_ok=True)
image_size = 1024
voxel_num_thre2d = 100
voxel_num_thre3d = 1000
names = sorted(os.listdir(gt_path))
print(f"ori \# files {len(names)=}")
names = [
name
for name in names
if os.path.exists(join(nii_path, name.split(gt_name_suffix)[0] + img_name_suffix))
]
print(f"after sanity check \# files {len(names)=}")
# set label ids that are excluded
remove_label_ids = [
12
] # remove deodenum since it is scattered in the image, which is hard to specify with the bounding box
tumor_id = None # only set this when there are multiple tumors; convert semantic masks to instance masks
# set window level and width
# https://radiopaedia.org/articles/windowing-ct
WINDOW_LEVEL = 40 # only for CT images
WINDOW_WIDTH = 400 # only for CT images
# %% save preprocessed images and masks as npz files
for name in tqdm(names[:40]): # use the remaining 10 cases for validation
image_name = name.split(gt_name_suffix)[0] + img_name_suffix
gt_name = name
gt_sitk = sitk.ReadImage(join(gt_path, gt_name))
gt_data_ori = np.uint8(sitk.GetArrayFromImage(gt_sitk))
# remove label ids
for remove_label_id in remove_label_ids:
gt_data_ori[gt_data_ori == remove_label_id] = 0
# label tumor masks as instances and remove from gt_data_ori
if tumor_id is not None:
tumor_bw = np.uint8(gt_data_ori == tumor_id)
gt_data_ori[tumor_bw > 0] = 0
# label tumor masks as instances
tumor_inst, tumor_n = cc3d.connected_components(
tumor_bw, connectivity=26, return_N=True
)
# put the tumor instances back to gt_data_ori
gt_data_ori[tumor_inst > 0] = (
tumor_inst[tumor_inst > 0] + np.max(gt_data_ori) + 1
)
# exclude the objects with less than 1000 pixels in 3D
gt_data_ori = cc3d.dust(
gt_data_ori, threshold=voxel_num_thre3d, connectivity=26, in_place=True
)
# remove small objects with less than 100 pixels in 2D slices
for slice_i in range(gt_data_ori.shape[0]):
gt_i = gt_data_ori[slice_i, :, :]
# remove small objects with less than 100 pixels
# reason: fro such small objects, the main challenge is detection rather than segmentation
gt_data_ori[slice_i, :, :] = cc3d.dust(
gt_i, threshold=voxel_num_thre2d, connectivity=8, in_place=True
)
# find non-zero slices
z_index, _, _ = np.where(gt_data_ori > 0)
z_index = np.unique(z_index)
if len(z_index) > 0:
# crop the ground truth with non-zero slices
gt_roi = gt_data_ori[z_index, :, :]
# load image and preprocess
img_sitk = sitk.ReadImage(join(nii_path, image_name))
image_data = sitk.GetArrayFromImage(img_sitk)
# nii preprocess start
if modality == "CT":
lower_bound = WINDOW_LEVEL - WINDOW_WIDTH / 2
upper_bound = WINDOW_LEVEL + WINDOW_WIDTH / 2
image_data_pre = np.clip(image_data, lower_bound, upper_bound)
image_data_pre = (
(image_data_pre - np.min(image_data_pre))
/ (np.max(image_data_pre) - np.min(image_data_pre))
* 255.0
)
else:
lower_bound, upper_bound = np.percentile(
image_data[image_data > 0], 0.5
), np.percentile(image_data[image_data > 0], 99.5)
image_data_pre = np.clip(image_data, lower_bound, upper_bound)
image_data_pre = (
(image_data_pre - np.min(image_data_pre))
/ (np.max(image_data_pre) - np.min(image_data_pre))
* 255.0
)
image_data_pre[image_data == 0] = 0
image_data_pre = np.uint8(image_data_pre)
img_roi = image_data_pre[z_index, :, :]
np.savez_compressed(join(npy_path, prefix + gt_name.split(gt_name_suffix)[0]+'.npz'), imgs=img_roi, gts=gt_roi, spacing=img_sitk.GetSpacing())
# save the image and ground truth as nii files for sanity check;
# they can be removed
img_roi_sitk = sitk.GetImageFromArray(img_roi)
gt_roi_sitk = sitk.GetImageFromArray(gt_roi)
sitk.WriteImage(
img_roi_sitk,
join(npy_path, prefix + gt_name.split(gt_name_suffix)[0] + "_img.nii.gz"),
)
sitk.WriteImage(
gt_roi_sitk,
join(npy_path, prefix + gt_name.split(gt_name_suffix)[0] + "_gt.nii.gz"),
)
# save the each CT image as npy file
for i in range(img_roi.shape[0]):
img_i = img_roi[i, :, :]
img_3c = np.repeat(img_i[:, :, None], 3, axis=-1)
resize_img_skimg = transform.resize(
img_3c,
(image_size, image_size),
order=3,
preserve_range=True,
mode="constant",
anti_aliasing=True,
)
resize_img_skimg_01 = (resize_img_skimg - resize_img_skimg.min()) / np.clip(
resize_img_skimg.max() - resize_img_skimg.min(), a_min=1e-8, a_max=None
) # normalize to [0, 1], (H, W, 3)
gt_i = gt_roi[i, :, :]
resize_gt_skimg = transform.resize(
gt_i,
(image_size, image_size),
order=0,
preserve_range=True,
mode="constant",
anti_aliasing=False,
)
resize_gt_skimg = np.uint8(resize_gt_skimg)
assert resize_img_skimg_01.shape[:2] == resize_gt_skimg.shape
np.save(
join(
npy_path,
"imgs",
prefix
+ gt_name.split(gt_name_suffix)[0]
+ "-"
+ str(i).zfill(3)
+ ".npy",
),
resize_img_skimg_01,
)
np.save(
join(
npy_path,
"gts",
prefix
+ gt_name.split(gt_name_suffix)[0]
+ "-"
+ str(i).zfill(3)
+ ".npy",
),
resize_gt_skimg,
)