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prepare_dataset.py
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prepare_dataset.py
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import sys
import os
from pathlib import Path
import glob
from configparser import ConfigParser
import pandas as pd
import numpy as np
import warnings
import pylidc as pl
from tqdm import tqdm
from statistics import median_high
from utils import is_dir_path,segment_lung
from pylidc.utils import consensus
from PIL import Image
warnings.filterwarnings(action='ignore')
# Read the configuration file generated from config_file_create.py
parser = ConfigParser()
parser.read('lung.conf')
#Get Directory setting
DICOM_DIR = is_dir_path(parser.get('prepare_dataset','LIDC_DICOM_PATH'))
MASK_DIR = is_dir_path(parser.get('prepare_dataset','MASK_PATH'))
IMAGE_DIR = is_dir_path(parser.get('prepare_dataset','IMAGE_PATH'))
CLEAN_DIR_IMAGE = is_dir_path(parser.get('prepare_dataset','CLEAN_PATH_IMAGE'))
CLEAN_DIR_MASK = is_dir_path(parser.get('prepare_dataset','CLEAN_PATH_MASK'))
META_DIR = is_dir_path(parser.get('prepare_dataset','META_PATH'))
#Hyper Parameter setting for prepare dataset function
mask_threshold = parser.getint('prepare_dataset','Mask_Threshold')
#Hyper Parameter setting for pylidc
confidence_level = parser.getfloat('pylidc','confidence_level')
padding = parser.getint('pylidc','padding_size')
class MakeDataSet:
def __init__(self, LIDC_Patients_list, IMAGE_DIR, MASK_DIR,CLEAN_DIR_IMAGE,CLEAN_DIR_MASK,META_DIR, mask_threshold, padding, confidence_level=0.5):
self.IDRI_list = LIDC_Patients_list
self.img_path = IMAGE_DIR
self.mask_path = MASK_DIR
self.clean_path_img = CLEAN_DIR_IMAGE
self.clean_path_mask = CLEAN_DIR_MASK
self.meta_path = META_DIR
self.mask_threshold = mask_threshold
self.c_level = confidence_level
self.padding = [(padding,padding),(padding,padding),(0,0)]
self.meta = pd.DataFrame(index=[],columns=['patient_id','nodule_no','slice_no','original_image','mask_image','malignancy','is_cancer','is_clean'])
def calculate_malignancy(self,nodule):
# Calculate the malignancy of a nodule with the annotations made by 4 doctors. Return median high of the annotated cancer, True or False label for cancer
# if median high is above 3, we return a label True for cancer
# if it is below 3, we return a label False for non-cancer
# if it is 3, we return ambiguous
list_of_malignancy =[]
for annotation in nodule:
list_of_malignancy.append(annotation.malignancy)
malignancy = median_high(list_of_malignancy)
if malignancy > 3:
return malignancy,True
elif malignancy < 3:
return malignancy, False
else:
return malignancy, 'Ambiguous'
def save_meta(self,meta_list):
"""Saves the information of nodule to csv file"""
tmp = pd.Series(meta_list,index=['patient_id','nodule_no','slice_no','original_image','mask_image','malignancy','is_cancer','is_clean'])
self.meta = self.meta.append(tmp,ignore_index=True)
def prepare_dataset(self):
# This is to name each image and mask
prefix = [str(x).zfill(3) for x in range(1000)]
# Make directory
if not os.path.exists(self.img_path):
os.makedirs(self.img_path)
if not os.path.exists(self.mask_path):
os.makedirs(self.mask_path)
if not os.path.exists(self.clean_path_img):
os.makedirs(self.clean_path_img)
if not os.path.exists(self.clean_path_mask):
os.makedirs(self.clean_path_mask)
if not os.path.exists(self.meta_path):
os.makedirs(self.meta_path)
IMAGE_DIR = Path(self.img_path)
MASK_DIR = Path(self.mask_path)
CLEAN_DIR_IMAGE = Path(self.clean_path_img)
CLEAN_DIR_MASK = Path(self.clean_path_mask)
for patient in tqdm(self.IDRI_list):
pid = patient #LIDC-IDRI-0001~
scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == pid).first()
nodules_annotation = scan.cluster_annotations()
vol = scan.to_volume()
print("Patient ID: {} Dicom Shape: {} Number of Annotated Nodules: {}".format(pid,vol.shape,len(nodules_annotation)))
patient_image_dir = IMAGE_DIR / pid
patient_mask_dir = MASK_DIR / pid
Path(patient_image_dir).mkdir(parents=True, exist_ok=True)
Path(patient_mask_dir).mkdir(parents=True, exist_ok=True)
if len(nodules_annotation) > 0:
# Patients with nodules
for nodule_idx, nodule in enumerate(nodules_annotation):
# Call nodule images. Each Patient will have at maximum 4 annotations as there are only 4 doctors
# This current for loop iterates over total number of nodules in a single patient
mask, cbbox, masks = consensus(nodule,self.c_level,self.padding)
lung_np_array = vol[cbbox]
# We calculate the malignancy information
malignancy, cancer_label = self.calculate_malignancy(nodule)
for nodule_slice in range(mask.shape[2]):
# This second for loop iterates over each single nodule.
# There are some mask sizes that are too small. These may hinder training.
if np.sum(mask[:,:,nodule_slice]) <= self.mask_threshold:
continue
# Segment Lung part only
lung_segmented_np_array = segment_lung(lung_np_array[:,:,nodule_slice])
# I am not sure why but some values are stored as -0. <- this may result in datatype error in pytorch training # Not sure
lung_segmented_np_array[lung_segmented_np_array==-0] =0
# This itereates through the slices of a single nodule
# Naming of each file: NI= Nodule Image, MA= Mask Original
nodule_name = "{}_NI{}_slice{}".format(pid[-4:],prefix[nodule_idx],prefix[nodule_slice])
mask_name = "{}_MA{}_slice{}".format(pid[-4:],prefix[nodule_idx],prefix[nodule_slice])
meta_list = [pid[-4:],nodule_idx,prefix[nodule_slice],nodule_name,mask_name,malignancy,cancer_label,False]
self.save_meta(meta_list)
np.save(patient_image_dir / nodule_name,lung_segmented_np_array)
np.save(patient_mask_dir / mask_name,mask[:,:,nodule_slice])
else:
print("Clean Dataset",pid)
patient_clean_dir_image = CLEAN_DIR_IMAGE / pid
patient_clean_dir_mask = CLEAN_DIR_MASK / pid
Path(patient_clean_dir_image).mkdir(parents=True, exist_ok=True)
Path(patient_clean_dir_mask).mkdir(parents=True, exist_ok=True)
#There are patients that don't have nodule at all. Meaning, its a clean dataset. We need to use this for validation
for slice in range(vol.shape[2]):
if slice >50:
break
lung_segmented_np_array = segment_lung(vol[:,:,slice])
lung_segmented_np_array[lung_segmented_np_array==-0] =0
lung_mask = np.zeros_like(lung_segmented_np_array)
#CN= CleanNodule, CM = CleanMask
nodule_name = "{}/{}_CN001_slice{}".format(pid,pid[-4:],prefix[slice])
mask_name = "{}/{}_CM001_slice{}".format(pid,pid[-4:],prefix[slice])
meta_list = [pid[-4:],slice,prefix[slice],nodule_name,mask_name,0,False,True]
self.save_meta(meta_list)
np.save(patient_clean_dir_image / nodule_name, lung_segmented_np_array)
np.save(patient_clean_dir_mask / mask_name, lung_mask)
print("Saved Meta data")
self.meta.to_csv(self.meta_path+'meta_info.csv',index=False)
if __name__ == '__main__':
# I found out that simply using os.listdir() includes the gitignore file
LIDC_IDRI_list= [f for f in os.listdir(DICOM_DIR) if not f.startswith('.')]
LIDC_IDRI_list.sort()
test= MakeDataSet(LIDC_IDRI_list,IMAGE_DIR,MASK_DIR,CLEAN_DIR_IMAGE,CLEAN_DIR_MASK,META_DIR,mask_threshold,padding,confidence_level)
test.prepare_dataset()