Imaging module tutorial

This tutorial shows how to explore the imaging parameters and perform a basic image quality check, convert DICOM into NRRD, check ROI segmentations, pre-process the images, and extract radiomic features.

Importing modules:

import os,sys
#from pmtool.ToolBox import ToolBox
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import numpy as np
#import seaborn as sb

%matplotlib inline

Data download

The Lung1 dataset (Aerts et al., 2014) contains pretreatment scans of 422 non-small cell lung cancer (NSCLC) patients, as well as manually deliniated gross tumor volume (GTV) for each patient, and clinical outcomes. More information you can find on the dataset web page and in the corresponding paper https://doi.org/10.1038/ncomms5006. For consistency, we recommend you to download the data to the '../data/dcms' folder of this project. You can find the data and its description (and the original paper) following the link below: https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics

To download the dataset, you might need to install the download manager: https://wiki.cancerimagingarchive.net/display/NBIA/Downloading+TCIA+Images#DownloadingTCIAImages-DownloadingtheNBIADataRetriever

Finally, you will have the '../data/dcms' folder with CT images (it will require 33 GB of disc space!).

Alternatively, TCIA provides a Python interface. To know more, see these examples https://github.com/kirbyju/TCIA_Notebooks/blob/main/TCIA_REST_API_Downloads.ipynb and follow the instructions below:

# installation - comment these lines if you have TCIA client installed!

!pip install --upgrade -q tcia-utils --user
!pip install --upgrade -q pandas --user

# setup

from tcia_utils import nbia
import requests

We are interested in getting "NSCLC-Radiomics" collection:

ds_metadata = nbia.getSeries(collection = "NSCLC-Radiomics")
# having a look at teh metadata
print(ds_metadata)

# downloading data for 3 patients, delete 'number = 3' to get all teh data
df = nbia.downloadSeries(ds_metadata, path ='../data/dcms/', format = "csv", number = 3)

2023-04-18 14:09:35,038:INFO:Calling... https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries with parameters {'Collection': 'NSCLC-Radiomics'}
2023-04-18 14:09:41,649:INFO:Downloading 1 out of 1265 Series Instance UIDs (scans).


[{'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.320898527671900265039224224949289088459', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.292318530655723826015592519561359458189', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2006-04-13 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-048', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 135, 'TimeStamp': '2020-10-07 14:01:08.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 71041260}, {'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.71621653125201582124240564508842688465', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.100855571832074152951605738408734618579', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2006-01-06 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-065', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 135, 'TimeStamp': '2020-10-07 14:00:50.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 71040974}, {'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.138522260934437218114778023563031054616', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.28825975190456185938017837904138557192', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2005-10-31 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-067', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 176, 'TimeStamp': '2020-10-07 14:01:28.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 92616766}, {'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.134645872977266948002680323417926540760', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.317339942450976650976837197851667935606', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2006-02-04 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-071', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 135, 'TimeStamp': '2020-10-07 14:01:33.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 71041240}, {'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.122579228305950125697741604889110154168', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.239287362785094903873779244680404401845', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2006-01-29 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-073', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 176, 'TimeStamp': '2020-10-07 14:01:30.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 92616782}, {'SeriesInstanceUID': 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'SeriesDate': '2005-12-22 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-079', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 'SoftwareVersions': 'VA70C', 'ImageCount': 135, 'TimeStamp': '2020-10-07 14:01:19.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 71040970}, {'SeriesInstanceUID': '1.3.6.1.4.1.32722.99.99.331857668923026632463450116440821077378', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.165984598763145101798157690702935033357', 'Modality': 'CT', 'ProtocolName': 'MAASTRO_PETCT_WholeBodyC', 'SeriesDate': '2006-02-22 00:00:00.0', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 0, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-082', 'Manufacturer': 'SIEMENS', 'ManufacturerModelName': 'Sensation 16', 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'1.3.6.1.4.1.32722.99.99.95262804700588909422498251982012748381', 'Modality': 'SEG', 'SeriesDate': '2020-08-12 00:00:00.0', 'SeriesDescription': 'Segmentation', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 300, 'Collection': 'NSCLC-Radiomics', 'PatientID': 'LUNG1-412', 'Manufacturer': 'QIICR', 'ManufacturerModelName': 'https://github.com/qiicr/dcmqi.git', 'SoftwareVersions': '3efde87', 'ImageCount': 1, 'TimeStamp': '2020-08-20 18:51:05.0', 'LicenseName': 'Creative Commons Attribution-NonCommercial 3.0 Unported License', 'LicenseURI': 'https://creativecommons.org/licenses/by-nc/3.0/', 'CollectionURI': 'https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI', 'FileSize': 26828234}, {'SeriesInstanceUID': '1.2.276.0.7230010.3.1.3.2323910823.20536.1597257528.824', 'StudyInstanceUID': '1.3.6.1.4.1.32722.99.99.322829785555422149405746581201628706557', 'Modality': 'SEG', 'SeriesDate': '2020-08-12 00:00:00.0', 'SeriesDescription': 'Segmentation', 'BodyPartExamined': 'LUNG', 'SeriesNumber': 300, 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2023-04-18 14:09:42,336:WARNING:Series 1.3.6.1.4.1.32722.99.99.320898527671900265039224224949289088459 already downloaded.
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2023-04-18 14:09:46,436:INFO:Downloading... https://services.cancerimagingarchive.net/nbia-api/services/v1/getImage?NewFileNames=Yes&SeriesInstanceUID=1.3.6.1.4.1.32722.99.99.331857668923026632463450116440821077378
2023-04-18 14:10:06,847:INFO:Downloaded 1 out of 1 requested series from a total of 1265 Series Instance UIDs (scans).
0 failed to download.
7 previously downloaded.
2023-04-18 14:10:06,853:INFO:Series metadata saved as downloadSeries_metadata_2023-04-18_1410.csv

CSV file will be found in the notebook directory and it should go to '../data/features' folder.

Dataset exploration

This functionality aims to retreive meta-information from the DICOM headers in order to explore the imaging parameters presented in the dataset.

Setting the parameters:

parameters = {'data_path': r'../data/dcms/', # path to your DICOM data
              'data_type': 'dcm', # original data format: DICOM
              'multi_rts_per_pat': False}   # when False, it will look only for 1 rtstruct in the patient folder, 
                                            # this will speed up the process, 
                                            # if you have more then 1 rtstruct per patient, set it to True

Initialize the dataset:

data_dcms = ToolBox(**parameters)

100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.14it/s]

Get the default metainformation from the DICOM files and print first 10:

dataset_description = data_dcms.get_dataset_description() 
dataset_description.head(10)

Patients processed: 100%|████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.26s/it]

You can check for the needed info by printing unique values of the tags:

print('Unique modalities found: ', np.unique(dataset_description.Modality.values)[0])

Unique modalities found:  CT

There are two pre-set parameter dictionaries for 'CT' and 'MRI' with the extended amount of parameters, parsed from the DICOM tags. These parameters are specific for these imaging modalities. You can also send a custom list of parameters. In this case the DICOM tags should have the same names as corresponding pydicom (DICOM keyword, https://pydicom.github.io/pydicom/dev/old/base_element.html).

Get the CT-specific metainformation from the DICOM files and print first 10:

ct_dcms = ToolBox(**parameters)
dataset_description = ct_dcms.get_dataset_description('CT') 
dataset_description.head(10)

100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.38it/s]
Patients processed: 100%|████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.04s/it]

Let's plot some of the scans parameters:

sb.set(context='poster', style='whitegrid')

study_date = sorted([ 'Nan' if x=='' or x=='NaN' else str(x[0:4]) for x in list(dataset_description['SeriesDate'])])[2:]
conv_kernel =['Nan' if x=='' or x=='NaN' else x for x in list(dataset_description['ConvolutionKernel'])]
tube_current =[-1 if x=='' or x=='NaN' else x for x in list(dataset_description['XRayTubeCurrent'])]
exposure =[-1 if x=='' or x=='NaN' else x for x in list(dataset_description['Exposure'])]
ps = sorted([(x[0]) for x in list(filter(lambda x: x != 'NaN', dataset_description['PixelSpacing'].values))])
sl_th = sorted([str(x)[0:3] for x in list(filter(lambda x: x != 'NaN', dataset_description['SliceThickness'].values))])
figures,descriptions = [study_date,conv_kernel,tube_current,exposure,ps,sl_th],['Study Date','Conv Kernel','Tube Current','Exposure','Pixel spacing','Slice Thickness']

fig,ax = plt.subplots(2,3,figsize=(25,15))
for i in range(2):
    for j in range(3):
        ax[i,j].hist(figures.pop(0),alpha=0.7)
        ax[i,j].set_title(descriptions.pop(0),fontsize=20)

Quality control step

This functionality aims to perform a simple quality check of the data and possibly to detect the wrong scans. These might be scans: of wrong imaging modality, with wrong imaging projections, with non-consistent (missing/overlapping) slices, with unsufficient amount of slices, with slice thickness inconsistent or out of the defined range, with pixel spacing out of range, with unknown or unacceptable konvolution kernel, with wrong axial plane resolution, with missing slope/intercept tags. To perform this check, the target scanning parameters are to be passed to the function. By removing/commenting some of the input parameters, the corresponding checks are disabled. For each patient, the output table contains the following flags: '1' - check passed, '0' - check failed.

qc_params = {'specific_modality': 'ct', # target modality: CT
            'thickness_range': [2,5], # slice thickness should be in range of 2..5 mm
            'spacing_range': [0.5,1.25], # pixel spacing should be in range of 0.5..1.25 mm
            'scan_length_range': [5,170], # scan should contain from 5 to 170 slices
            'axial_res': [512,512], # the axial resolution should be 512x512
            'kernels_list': ['standard','lung','b19f']} # the following kernels are acceptable

qc_dataframe = ct_dcms.get_quality_checks(qc_params)

 67%|████████████████████████████████████████████████████████                            | 2/3 [00:03<00:01,  1.51s/it]

Cannot perform conv kernel check for pat: LUNG1-002_20180526_CT_1


100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.42s/it]

Cannot perform conv kernel check for pat: LUNG1-003_20180209_CT_1

qc_dataframe

DICOM to NRRD conversion

To convert DICOM dataset to the volume (nrrd) format, set up the parameters:

• 'export_path = ...' export path where the converted NRRDs will be placed,
• 'region_of_interest = ...' if you know exact name of the ROI you want to extract, then write it with the '!' character in front, eg. 'region_of_interest = !gtv1',
• if you want to extract all the GTVs in the rtstruct eg. 'gtv1', 'gtv2', 'gtv_whatever', then just specify the stem word, eg. 'region_of_interest = gtv',
• default value is 'region_of_interest ='all'', where all ROI's in rtstruct will be extracted.

export_path =r'../data/' # the function will create 'converted_nrrd' folder in the specified directory

Initialize the dataset (originally downloaded directory with DICOM files):

data_ct = ToolBox(**parameters) 

100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00,  2.49it/s]

Run the conversion:

data_ct.convert_to_nrrd(export_path, 'gtv')

Patients converted: 100%|████████████████████████████████████████████████████████████████| 3/3 [00:08<00:00,  2.82s/it]

Quick check of the ROI's in the NRRD dataset

If you want to check your converted images & ROIs, or just any volumetric dataset (NRRD/MHA), you can use get_jpegs function of the toolbox. It will generate JPEG images for each slice with the image and overlap of the contour.

Initialize the dataset (converted NRRD files):

data_ct_nrrd = ToolBox(data_path = r'../data/converted_nrrds/', data_type='nrrd')

100%|██████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1000.23it/s]

Run the visualization:

data_ct_nrrd.get_jpegs(r'../data/') # the function will create 'images_quick_check' folder in the specified directory 

Patients processed: 100%|████████████████████████████████████████████████████████████████| 5/5 [00:57<00:00, 11.42s/it]

Let's check one of the patients:

from ipywidgets import interact
import numpy as np
from PIL import Image

def browse_images(images,names):
    n = len(images)
    def view_image(i):
        plt.figure(figsize=(20,10))
        plt.imshow(images[i])#, cmap=plt.cm.gray_r, interpolation='nearest')
        plt.title('Slice: %s' % names[i])
        plt.axis('off')
        plt.show()
    interact(view_image, i=(0,n-1))

for pat,_ in data_ct_nrrd:
    _,file_struct = [*os.walk(os.path.join('../data/images_quick_check/',pat))]
    root,images = file_struct[0],file_struct[2]
    imgs =[np.array(Image.open(os.path.join(root,img))) for img in images]
    print(pat)
    browse_images(imgs,images)
    break

LUNG1-001_20180209_CT_2_GTV-1_mask



interactive(children=(IntSlider(value=10, description='i', max=20), Output()), _dom_classes=('widget-interact'…

Image pre-processing

Initialize the dataset (converted NRRD files):

data_ct_nrrd = ToolBox(data_path = r'../data/converted_nrrds/',
                    data_type='nrrd')

100%|██████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1501.00it/s]

sb.set(context='poster', style='white')
data_ct_nrrd.pre_process(ref_img_path = '../data/converted_nrrds/LUNG1-001_20180209_CT_2/image.nrrd',
                         save_path = '../data/nrrd_preprocessed',
                         hist_match = False,        # boolean
                         subcateneus_fat = False,   # boolean
                         fat_value = 774,           # this is a dummy value, 
                                                    # you would need to find that value for each image
                         percentile_scaling = False, # boolean
                         window_filtering_params = (1500,-600), #Window Filtering [WW,WL]
                         binning = 255,             # this is a dummy value, it takes False or int 
                                                    # (# of bins for intensity resampling)
                         verbosity = True,          # boolean
                         z_score = True,           # boolean 
                         hist_equalize = True,     # boolean
                         norm_coeff = (1000.,500.), # these are dummy values, you would need to estimate real mu and sigma
                                                    # it takes None or tuple: (mu,sigma)
                         visualize = True)

  0%|                                                                                            | 0/5 [00:00<?, ?it/s]

----------------------------------------
Original image stats:
Number of unique intensity values : 3033
Intensity values range:[-1024:3034]
Image intensity dtype: int16
----------------------------------------

Window limits  -1000 150.0
Window filtering applied

min:  0 max:  1150 step:  4.509803921568627
Resampling with a step of:  4.509803921568627 Amount of unique values, original img:  1151 resampled img:  255
----------------------------------------
Intensity values resampled with a number of bins: 255
----------------------------------------

Z-score normalization applied based on the whole image, Mu=1000.0, sigma=500.0

 20%|████████████████▊                                                                   | 1/5 [00:36<02:26, 36.66s/it]

----------------------------------------
Original image stats:
Number of unique intensity values : 3019
Intensity values range:[-1024:3071]
Image intensity dtype: int16
----------------------------------------

Window limits  -1000 150.0
Window filtering applied

min:  0 max:  1150 step:  4.509803921568627
Resampling with a step of:  4.509803921568627 Amount of unique values, original img:  1151 resampled img:  255
----------------------------------------
Intensity values resampled with a number of bins: 255
----------------------------------------

Z-score normalization applied based on the whole image, Mu=1000.0, sigma=500.0

 40%|█████████████████████████████████▌                                                  | 2/5 [01:05<01:36, 32.28s/it]

----------------------------------------
Original image stats:
Number of unique intensity values : 3829
Intensity values range:[-1024:3071]
Image intensity dtype: int16
----------------------------------------

Window limits  -1000 150.0
Window filtering applied

min:  0 max:  1150 step:  4.509803921568627
Resampling with a step of:  4.509803921568627 Amount of unique values, original img:  1151 resampled img:  255
----------------------------------------
Intensity values resampled with a number of bins: 255
----------------------------------------

Z-score normalization applied based on the whole image, Mu=1000.0, sigma=500.0

 60%|██████████████████████████████████████████████████▍                                 | 3/5 [01:35<01:01, 30.95s/it]

----------------------------------------
Original image stats:
Number of unique intensity values : 3829
Intensity values range:[-1024:3071]
Image intensity dtype: int16
----------------------------------------

Window limits  -1000 150.0
Window filtering applied

min:  0 max:  1150 step:  4.509803921568627
Resampling with a step of:  4.509803921568627 Amount of unique values, original img:  1151 resampled img:  255
----------------------------------------
Intensity values resampled with a number of bins: 255
----------------------------------------

Z-score normalization applied based on the whole image, Mu=1000.0, sigma=500.0

 80%|███████████████████████████████████████████████████████████████████▏                | 4/5 [02:03<00:29, 29.96s/it]

----------------------------------------
Original image stats:
Number of unique intensity values : 3829
Intensity values range:[-1024:3071]
Image intensity dtype: int16
----------------------------------------

Window limits  -1000 150.0
Window filtering applied

min:  0 max:  1150 step:  4.509803921568627
Resampling with a step of:  4.509803921568627 Amount of unique values, original img:  1151 resampled img:  255
----------------------------------------
Intensity values resampled with a number of bins: 255
----------------------------------------

Z-score normalization applied based on the whole image, Mu=1000.0, sigma=500.0

100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [02:33<00:00, 30.67s/it]

PyRadiomics features extraction

In our toolbox, we are using PyRadiomics software (https://pyradiomics.readthedocs.io/en/latest/) to extract the features. You can read the full documentation for the currently stable version: https://pyradiomics.readthedocs.io/_/downloads/en/stable/pdf/.

We are using PyRadiomics parameters file customized for CT data:

parameters = r"example_ct_parameters.yaml"
features = data_ct_nrrd.extract_features(parameters, loggenabled=True)

Patients processed:  40%|█████████████████████████▌                                      | 2/5 [00:14<00:22,  7.49s/it]C:\Users\e.lavrova\AppData\Local\Continuum\anaconda3\lib\site-packages\pmtool\ToolBox.py:146: UserWarning: region : LUNG1-003_20180209_CT_1_GTV-1_mask skipped
  warn('region : %s skipped'%pat)
Patients processed: 100%|████████████████████████████████████████████████████████████████| 5/5 [00:26<00:00,  5.40s/it]

Printing the features for first 3 ROIs:

features.head(3)

Writing the features into the Excel file (you will find it in the 'data/features' folder):

writer = pd.ExcelWriter('../data/features/extracted_features.xlsx') 
features.to_excel(writer, 'Sheet1')
writer.save()