0.下载

0.0 链接汇总

• LIDC-IDRI官方网址：https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LIDC-IDRI
• NBIA Data Retriever 下载链接：https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+TCIA+Images

0.1 步骤

1. 检索
   分成两种，Simple Search一个是多种关键字筛选，鉴定为没用。
   
   直接用Text Search ，将annotation的ID输上，点击search
   
2. 加入Cart
   检索出来会有好几种模态/任务的数据，选择自己需要点击购物车加入Cart。
   例如：我是做CT分割，故只选择模态为CT的那个数据。
   
   重复Text检索步骤，得到最终自己需要的所有Cart：
   
3. 下载

• 安装好NBIA Data Retriever
  前面链接下载，或者Download->Get NBIA Data Retriever 下载，有官方指引。
  

• 生成manifest文件
  

• 下载

设置好路径，点击start

1.合成CT图

这边是直接偷了NaviAirwayi的代码进行dicom文件merge成nii文件。
文件结构需要为：

如果按照之前步骤进行下载的话，获得的文件就是上述结构。只是子文件名称会因为太长而被修改，不过不影响结果，最终生成文件名是按照一级目录命名。

预处理代码完整如下：

import numpy as np
import os
import SimpleITK as sitk
from PIL import Image
import pydicom
import cv2
import nibabel as nib
import pydicom

## funtion
#####-----------------------------------------------------------------------

def loadFile(filename):
    ds = sitk.ReadImage(filename)
    #pydicom.dcmread(filename)
    img_array = sitk.GetArrayFromImage(ds)
    frame_num, width, height = img_array.shape
    #print("frame_num, width, height: "+str((frame_num, width, height)))
    return img_array, frame_num, width, height

'''
def loadFileInformation(filename):
    information = {}
    ds = pydicom.read_file(filename)
    information['PatientID'] = ds.PatientID
    information['PatientName'] = ds.PatientName
    information['PatientSex'] = ds.PatientSex
    information['StudyID'] = ds.StudyID
    information['StudyDate'] = ds.StudyDate
    information['StudyTime'] = ds.StudyTime
    information['Manufacturer'] = ds.Manufacturer
    return information
'''

def get_3d_img_for_one_case(img_path_list, img_format="dcm"):
    img_3d=[]
    for idx, img_path in enumerate(img_path_list):
        print("progress: "+str(idx/len(img_path_list))+"; "+str(img_path), end="\r")
        img_slice, frame_num, _, _ = loadFile(img_path)
        assert frame_num==1
        img_3d.append(img_slice)
    img_3d=np.array(img_3d)
    return img_3d.reshape(img_3d.shape[0], img_3d.shape[2], img_3d.shape[3])
#####-----------------------------------------------------------------------

# the path to LIDC-IDRI raw images

LIDC_IDRI_raw_path = "G:\BAS_test_raw\manifest-1708937949454\LIDC-IDRI"

LIDC_IDRI_raw_img_dict = {}
img_names = os.listdir(LIDC_IDRI_raw_path)
img_names.sort()
img_names

path_to_a_case = ""


def find_imgs(input_path):
    global path_to_a_case
    items = os.listdir(input_path)
    items.sort()
    # print("There are "+str(items)+" in "+str(input_path))
    All_file_flag = True
    for item in items:
        if os.path.isdir(input_path + "/" + item):
            All_file_flag = False
            break
    if All_file_flag and len(items) > 10:
        # print("we get "+str(input_path))
        path_to_a_case = input_path
    else:
        for item in items:
            if os.path.isdir(input_path + "/" + item):
                # print("open filefloder: "+str(input_path+"/"+item))
                find_imgs(input_path + "/" + item)


for idx, img_name in enumerate(img_names):
    print(idx / len(img_names), end="\r")
    find_imgs(LIDC_IDRI_raw_path + "/" + img_name)
    slice_names = os.listdir(path_to_a_case)
    slice_names.sort()
    LIDC_IDRI_raw_img_dict[img_name] = []
    for slice_name in slice_names:
        if slice_name.split(".")[1] == "dcm":
            LIDC_IDRI_raw_img_dict[img_name].append(path_to_a_case + "/" + slice_name)

print("Show the case names: "+str(LIDC_IDRI_raw_img_dict.keys()))


# set output path

output_image_path = r"G:\myBAS\test\images"
if not os.path.exists(output_image_path):
    os.mkdir(output_image_path)

for case in LIDC_IDRI_raw_img_dict.keys():
    img_3d = get_3d_img_for_one_case(LIDC_IDRI_raw_img_dict[case])
    sitk.WriteImage(sitk.GetImageFromArray(img_3d),
                    output_image_path + "/" + case + ".nii.gz")

今天折腾了半死，希望对大家有帮助。

reference

refer1