Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 458
  • Home
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 5  |  Issue : 1  |  Page : 13-21

Statistical survey of open source medical image databases on the Internet


1 School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
2 Institute of Digital Medicine, Biomedical Engineering College, Third Military Medical University, Chongqing, China

Date of Web Publication29-May-2019

Correspondence Address:
Bin Zhang
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, No. 2, Linggong Road, Dalian, Liaoning, 116024
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_1_19

Rights and Permissions
  Abstract 


Background and Objectives: Nowadays, more and more open source medical imaging databases are published on the Internet for medical teaching, algorithm development, and medical research. However, a statistical survey of these databases is still lacking. In this survey, we summarize the current status of open source medical image databases on the Internet. The aim is to make it easier for everyone to find and use open source medical image data. Methods: Information about publicly available medical image databases was collected by searching for scientific papers and Internet search engines. Based on the collected information, the number of databases and the number of images were counted for different diseases, body parts, imaging modalities, and countries. Results: Cancer, particularly breast cancer and lung cancer, ranked top in database numbers among all diseases. The breast, brain, lung, and chest are the top four body parts in terms of database numbers. Computed tomography, magnetic resonance imaging, and X-ray are the most common imaging modalities in the open source datasets. The USA and the Netherlands are the top two countries who own the most databases. Conclusions: The rankings for diseases and body parts were closely related to the diseases morbidity and the health-care expenditure of a country. The number of open sources of medical imaging databases is still growing; there is a need for continuous statistical research on their existence status in the coming years. The list of all the collected databases is opened on the Internet (https://docs.qq.com/sheet/DQWF0QlZKVHpHU1Za).

Keywords: Databases, medical image, open source


How to cite this article:
Wang H, Ma X, Zhai H, Liao Y, Wu Y, Chen N, Zhang S, Zhang B. Statistical survey of open source medical image databases on the Internet. Digit Med 2019;5:13-21

How to cite this URL:
Wang H, Ma X, Zhai H, Liao Y, Wu Y, Chen N, Zhang S, Zhang B. Statistical survey of open source medical image databases on the Internet. Digit Med [serial online] 2019 [cited 2019 Aug 23];5:13-21. Available from: http://www.digitmedicine.com/text.asp?2019/5/1/13/259306




  Introduction Top


The field of medical imaging research is entering the era of big data. Medical teaching, machine learning, and disease investigation all required large amounts of medical images as the supporting data. The recent surge of deep learning technique also needs big medical image datasets for algorithm development. To meet these requirements, more and more publicly available medical images databases are opened on the Internet, such as the databases of the Cancer Imaging Archive (TCIA)[1],[2] and the Grand Challenges in MICCAI Biomedical Image Analysis.[3] These opened datasets have greatly promoted medical imaging research and industrial products development. For example, since the first establishment of the MICCAI Grand Challenges in 2007, 189 challenges with various types of medical images have been opened on the challenge website. These challenges facilitated a direct and fair comparison of different medical image analysis algorithms and resulted in hundreds of published papers on Medical Imaging Journals and Conference Proceedings. As another famous example, the Alzheimer's Disease Neuroimaging Initiative[4],[5] project has shared large amounts of magnetic resonance imaging (MRI) and PET images of the Alzheimer's disease along with the data of genetics, cognitive tests, CSF, and blood biomarker predictors. This datasets facilitated collaborations on the Alzheimer's disease research and promoted the development of many Alzheimer's brain image analysis algorithms.

Despite the worldwide efforts on establishing open source medical image data, there is barely any statistical survey on the existing status of these databases. The lacking of such a survey may cause difficulty in finding proper data for scientific research, as well as resulting in unsatisfactory generalization ability of the algorithms developed based on limited training data. To give a comprehensive review of the current open source medical images, we conducted an organized survey by collecting the information on publicly available databases of medical images on the Internet. With this survey, we report to the research community about the existing status of the open source medical image databases and hope to promote the related studies in the field of medical imaging.


  Methods Top


Database selection criterion

This study mainly focuses on medical image data. The criteria for distinguishing medical images from other types of images were the imaging modality. We mainly collect the clinical medical imaging modalities, including computed tomography (CT), MRI, X-ray, ultrasound, nuclear medicine imaging, and microscopic histology. Another judgment criterion was the format of the images. We collected the databases, including the formats of digital imaging and communications in medicine (DICOM), nifty, nrrd, analyze and the metadata format (i.e., the mha and mhd formats).

It should be noted that there are many open source database of nonmedical images and nonimage medical data. Typical examples of the nonmedical image databases include the ImageNet[6] of over 14 million natural images and the biological image databases of cells, microcosmic structure, and small animals. Examples of the nonimage medical database include the biology datasets of the human genome,[7] medical signals of electroencephalogram,[8] and electrocardiogram,[8] etc. There are also some medical image databases of medical figures, charts, and graphs extracted from published scientific papers, such as Open-I[9] database established by the U. S. National Library of Medicine, including over 3.7 million images collected from 1.2 million PubMed Central articles. These images were article figures rather than medical image data, therefore are not included in this survey.

Database collection

Databases were collected through manually searching the keywords of “database,” “dataset,” “http”, “www,” “open source,” and “public” from academic papers and Internet search engines (e.g., Bing, Baidu, and Google). Academic papers between the year 2000 and 2018 were downloaded from the PubMed database[10] and medical imaging conference proceedings (including the MICCAI International Conference,[11] the IEEE International Symposium on Biomedical Imaging[12] and the International Society for Optics and Photonics on Medical Imaging[13]). When a database was found to meet our criterion, the inter-linkages on its website was also manually traced to find other databases linked to the found one.

In total, we found 200 databases, 31% of which were freely open and 69% requires user account registration. From the text description on the websites, we collected detailed information about image numbers, disease type, body part, image modality, and database country. The list of all the collected databases is opened on the Internet at https://docs.qq.com/sheet/DQWF0QlZKVHpHU1Za, the user needs to register an account from https://docs.qq.com to access the list.

Statistical analysis

Based on the collected information, the number of databases and image series were counted for different disease types, body parts, image modalities, and countries. The terminology “image series” refers to a DICOM series, a three-dimensional volumetric image or a dynamic image, which included multiple slices belonging to a single scan. While performing the statistical analysis, we found it a common case that one database or one image series includes multiple disease types, body parts, or image modalities. In such a case, we increased the counting number by one for each disease type, body part, or image modality. However, if a database is owned by multiple countries (e.g., K countries), we added 1/K to the number of databases of each country, meaning that each county only partially owns the database. As a result, the country numbers can be a noninteger.


  Results Top


In this section, the numbers of databases and image series for different disease types, body parts, image modalities, and countries are reported. Detailed discussion about of the results will be presented in the discussion section.

Statistics of disease types

[Figure 1]a and [Figure 1]b plot the number of databases and image series for different disease types, respectively. The disease names on the abscissa axes were adopted from the disease descriptions on the database website. It can be observed that the top four diseases in database number are lung cancer, breast cancer, prostate cancer, and brain cancer. The “unknown cancer,” lung cancer,[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30] Alzheimer's disease,[5],[31],[32],[33],[34],[35] and breast cancer[36],[37],[38],[39],[40],[41],[42],[43],[44] are the top four in terms of image series number. The terminology “unknown cancer” refers to that there was no detailed information about cancer's name in the database website. It is interesting to see that Alzheimer's disease ranked third place in image series number, but only the sixth place in database number. This is because there were some databases (e.g., ANDI,[4],[5] OASIS,[45],[46],[47],[48],[49],[50] and CRHD[51],[52],[53],[54],[55]) including a large amount of Alzheimer's disease images.
Figure 1: The number of (a) databases and (b) image series of different disease types. The colors of the bars represent the hospital department that a disease belongs to

Click here to view


Statistics of body parts

[Figure 2]a and [Figure 2]b demonstrate the number of databases and image series for different body parts, respectively. The body part names on the abscissa axes were adopted from the disease descriptions on the database website. Please note that for the breast item, we classified the breast carcinoma to the breast cancer for figure clarity.
Figure 2: The number of (a) databases and (b) image series of different body parts. The bar colors represent the anatomical ontology system that a body part belongs to

Click here to view


In terms of database number, the top four body parts are brain,[45],[48],[56],[57],[58],[59],[60],[61],[62],[63],[64],[65],[66] lung,[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30],[67] breast,[36],[37],[38],[39],[40],[41],[42],[43],[44] and heart.[68],[69] For the number image series, the top four are chest,[70],[71],[72],[73] neuron,[74] brain, and breast. The term “chest” differs from “lungs” since “chest” stands for the screen of the entire thorax region (such as in chest X-ray scans). The term “neuron” differs from “brain” because “neuron” mainly refers to the histology images of neuron cells, whereas “brain” means the clinical brain scan using CT, MR, or nuclear medicine scanners.

Statistics of image modalities

[Figure 3]a and [Figure 3]b illustrate the percentage of databases and image series for different image modalities, respectively. The abbreviation used in the figure came from the “DICOM Modality Abbreviations” in the Cancer Imaging Archive.[75] In these results, the modalities “CR,” “DX,” “radiography” were all classified to “X-ray.”[75] The terminology “synthetic images” refer to the artificially created medical images through computer simulation.
Figure 3: The percentage of (a) databases and (b) image series of different image modalities

Click here to view


It is obvious that MR,[28],[37],[38],[39],[58],[63],[76],[77],[78],[79] CT,[18],[26],[27],[71],[80],[81],[82],[83],[84],[85],[86] and X-ray[70],[73] rank the top three for both database number and image series number, meaning that they are the most studied modalities. These three modalities occupy >75% of all databases and over 85% of all image series. The X-ray modality takes up only 9.29% of the total database number, but 32.96% of the image series number, since it is easier to collect a large number of X-ray images than CT and MR images.

Statistics of countries

[Figure 4]a indicates the number of databases belonging to different countries. The top three countries are the USA, the Netherlands and the UK. [Figure 4]b indicates the number of image series belonging to different countries and the top three is the USA, the Netherlands, and China. In both [Figure 4]a and [Figure 4]b, the USA and the Netherlands ranked the first two places, implying good activities of medical data sharing in these countries. China ranks the sixth in database number and the third in image series number thanks to its large population basis.
Figure 4: The number of (a) databases and (b) image series of different countries

Click here to view



  Discussion Top


From the statistical results, it is evident to see that the disease and body parts attracting the most healthcare concerns tend to have the most databases and image series. For example, the brain, breast, chest, and lung are the four mostly concerned body parts, because dementia diseases, breast cancer, and lung cancer are the most common death causing diseases worldwide.[87],[88] It is worth mentioning that the brain was a big highlight in recent year's studies. For example, the Human Brain Project was launched by the European Commission's Future and Emerging Technologies scheme in October 2013 and was scheduled to run for 10 years.[89] All the well-concerned brain diseases (e.g., the Alzheimer's disease[11],[5],[31],[32],[33],[34],[35] mental disorders,[51],[90] brain cancer,[91] multiple sclerosis,[57],[92],[93] low-grade and high-grade glioma, temporal lobe epilepsy,[55] low-grade glioma,[94] and attention deficit hyperactivity disorder[95]) all contributed to the increase of database numbers.

The databases of chest images constituted a big part of the open source databases. It can be observed that the number of chest databases ranked the fourth place in all the body parts and the number of chest images ranked the first. The chest X-ray was one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.[73] Recently, the National Institutes of Health of the USA opened a new chest X-ray database, namely “ChestX-ray8”[73] comprising 108,948 frontal-view X-ray images of 32,717 unique patients. This database became the biggest database of chest X-ray in patient number. Another big database was the clinical trial of screening tests for lung cancer controlled by the National Lung Screening Trial.[96] Approximately 54,000 participants were enrolled between August 2002 and April 2004.[96] In this database, the number of series was 203,099 and the number of patients was 26,254.

Recently, the databases of lung images increased rapidly. In this survey, the number of lung databases ranked the second and the number of lung images ranked seventh. The collected lung diseases in thee databases included lung cancer,[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30] pneumopathy,[18],[69],[97],[98],[99] lung adenocarcinomas,[20] tuberculosis,[100],[101] lung nodule,[18],[26],[29],[30],[102] lung squamous cell carcinoma,[103] and pulmonary thrombosis. Lung cancer is the most frequent disease type in all lung diseases, because it is the leading cause of death[104] and it can be imaged through commonly available imaging modalities such as chest radiographs and CT scans.[105]

As for the countries, the USA ranked the first and exceeds the second country significantly. In line with statistics of the World Health Organization in 2014, total expenditure on health as of GDP of the USA was 17.1%, whereas for China, it was 5.5%. Total expenditure on health per capita of the USA was $9403, whereas for China, it was $731. For the Netherlands, the total expenditure on health as of GDP was 10.9% and total expenditure on health per capita was $5202. The above two statistics for the UK were 9.1% and $3377, whereas for Italy were 9.2% and $3239.[106] It is interesting to see that the ranking of open source database number and image series number is in good correlation with a country's expenditure on the health-care system.

Since this survey study is based on a manual search from the Internet, it is impossible to cover all the open source medical image databases. For future research, it is necessary to develop an automatic web searching scheme (e.g., the web Crowley) to guarantee more comprehensive database collection. As the field of medical imaging is evolving rapidly, there will be an increasing number of new databases and our survey will be performed yearly to keep up with the pace of database growth.


  Conclusions Top


With this research, we hope to provide statistical information of existing open source medical image databases and aid the researchers to find proper datasets for their medical imaging research. Our statistical results showed that breast cancer and lung cancer are the top two diseases in terms of database number. The ranking of the diseases was closely related to the diseases' morbidity. The health-care expenditure of a country had a significant impact on its number of databases. Regarding the imaging modalities, >75% of the modalities were CT, MR, and X-ray. The number open source medical image databases is growing fast. Our future search will focus on developing automatic web crawlers to collect the information of emerging new databases.

Acknowledgment

The authors would like to thank Mr. Zhonghua Chen for the supporting suggestions on the database information collection.

Financial support and sponsorship

This study was supported by the youth program of the National Natural Science Fund of China (No. 81401475), the general program of the National Natural Science Fund of China (No. 61571076), the general program of Liaoning Science and Technology Project (No. 2015020040), the cultivating program of the Major National Natural Science Fund of China (No. 91546123), the National Key Research and Development Program (No. 2016YFC0103101, 2016YFC0103102, and 2016YFC0106402), the Science and Technology Star Project Fund of Dalian City (No. 2016RQ019), and the Basic Research Funding and Xinghai Scholar Cultivating Funding of Dalian University of Technology (No. DUT14RC(3)066, DUT16RC(3)099, and DUT15 LN02).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J Digit Imaging 2013;26:1045-57.  Back to cited text no. 1
    
2.
The Cancer Imaging Archive (TCIA) Public Access. TCIA Collections; 2019. Available from: http://www.cancerimagingarchive.net/. [Last accessed on 2019 Mar 09].  Back to cited text no. 2
    
3.
Grand Challenges in Biomedical Image Analysis; 2019. Available from: https://www.grand-challenge.org/. [Last accessed on 2019 Mar 09].  Back to cited text no. 3
    
4.
Cognitive Atlas; 2019. Available from: http://www.cognitiveatlas.org/. [Last accessed on 2019 Mar 09].  Back to cited text no. 4
    
5.
Alzheimer's Disease Neuroimaging Initative. DATA & SAMPLES; 2019. Available from: http://www.adni.loni.usc.edu/data-samples/. [Last accessed on 2019 Mar 09].  Back to cited text no. 5
    
6.
ImageNet; 2019. Available from: http://www.image-net.org/about-overview. [Last accessed on 2019 Mar 09].  Back to cited text no. 6
    
7.
Encyclopædia Britannica. Human Genome; 2019. Available from: https://www.britannica.com/science/human-genome. [Last accessed on 2019 Mar 09].  Back to cited text no. 7
    
8.
PhysioBank Databases; 2019. Available from: http://www.physionet.org/physiobank/database/. [Last accessed on 2019 Mar 09].  Back to cited text no. 8
    
9.
Open Access Biomedical Image Search Engine. Image Type; 2019. Available from: https://www.openi.nlm.nih.gov/faq.php?it=xg. [Last accessed on 2019 Mar 09].  Back to cited text no. 9
    
10.
US National Library of Medicine. National Institutes of Health; 2019. Available from: https://www.ncbi.nlm.nih.gov/pubmed/. [Last accessed on 2019 Mar 09].  Back to cited text no. 10
    
11.
MICCAI Conferences; 2019. Available from: http://www.miccai.org/conferences. [Last accessed on 2019 Mar 09].  Back to cited text no. 11
    
12.
IEEE International Symposium on Biomedical Imaging; 2019. Available from: https://www.embs.org/events/ieee-international-symposium-on-biomedical-imaging-isbi-2019/. [Last accessed on 2019 Mar 09].  Back to cited text no. 12
    
13.
SPIE, The International Society for Optics and Photonics. The Leading Conference that Explores the Science of Medical Imaging. SPIE, The International Society for Optics and Photonics; 2019. Available from: http://www.spie.org/conferences-and-exhibitions/medical-imaging. [Last accessed on 2019 Mar 09].  Back to cited text no. 13
    
14.
Roman NO, Shepherd W, Mukhopadhyay N, Hugo GD, Weiss E. Interfractional positional variability of fiducial markers and primary tumors in locally advanced non-small-cell lung cancer during audiovisual biofeedback radiotherapy. Int J Radiat Oncol Biol Phys 2012;83:1566-72.  Back to cited text no. 14
    
15.
Balik S, Weiss E, Jan N, Roman N, Sleeman WC, Fatyga M, et al. Evaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy. Int J Radiat Oncol Biol Phys 2013;86:372-9.  Back to cited text no. 15
    
16.
Hugo GD, Weiss E, Sleeman WC, Balik S, Keall PJ, Lu J, et al. Alongitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med Phys 2017;44:762-71.  Back to cited text no. 16
    
17.
Hugo GD, Weiss E, Sleeman WC, Balik S, Keall PJ, Lu J, et al. Data from 4D Lung Imaging of NSCLC Patients. The Cancer Imaging Archive; 2016.  Back to cited text no. 17
    
18.
Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Med Phys 2011;38:915-31.  Back to cited text no. 18
    
19.
Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. Data From LIDC-IDRI. The Cancer Imaging Archive; 2015.  Back to cited text no. 19
    
20.
Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One 2015;10:e0118261.  Back to cited text no. 20
    
21.
Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, et al. Data from: Quantitative Computed Tomographic Descriptors Associate tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. Cancer Imaging Archive; 2015.  Back to cited text no. 21
    
22.
Kalpathy-Cramer J, Napel S, Goldgof D, Zhao B. Multi-site Collection of Lung CT Data with Nodule Segmentations. Cancer Imaging Archive; 2015. Available from: http://doi.org/10.7937/K9/TCIA.2015.1BUVFJR7. [Last accessed on 2019 Mar 09].  Back to cited text no. 22
    
23.
Zhao B. Data From Lung_Phantom. Cancer Imaging Archive; 2015.  Back to cited text no. 23
    
24.
Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, et al. Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data – Methods and preliminary results. Radiology 2012;264:387-96.  Back to cited text no. 24
    
25.
Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.  Back to cited text no. 25
    
26.
Gavrielides MA, Kinnard LM, Myers KJ, Peregoy J, Pritchard WF, Zeng R, et al. Aresource for the assessment of lung nodule size estimation methods: Database of thoracic CT scans of an anthropomorphic phantom. Opt Express 2010;18:15244-55.  Back to cited text no. 26
    
27.
Zhao B, James LP, Moskowitz CS, Guo P, Ginsberg MS, Lefkowitz RA, et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 2009;252:263-72.  Back to cited text no. 27
    
28.
Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015;60:5471-96.  Back to cited text no. 28
    
29.
Armato SG 3rd, Drukker K, Li F, Hadjiiski L, Tourassi GD, Engelmann RM, et al. LUNGx challenge for computerized lung nodule classification. J Med Imaging (Bellingham) 2016;3:044506.  Back to cited text no. 29
    
30.
Armato SG 3rd, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, et al. LUNGx challenge for computerized lung nodule classification: Reflections and lessons learned. J Med Imaging (Bellingham) 2015;2:020103.  Back to cited text no. 30
    
31.
Connectome Coordination Facility. CRHD Alzheimer's Disease Connectome Project. Connectome Coordination Facility; 2019. Available from: https://www.humanconnectome.org/study/alzheimers-disease-connectome-project. [Last accessed on 2019 Mar 09].  Back to cited text no. 31
    
32.
Minimal Interval Resonance Imaging in Alzheimer's Disease; 2019. Available from: https://www.ucl.ac.uk/drc/research/methods/minimal-interval-resonance-imaging-alzheimers-disease-miriad. [Last accessed on 2019 Mar 09].  Back to cited text no. 32
    
33.
OASIS Brains Datasets; 2019. Available from: http://www.oasis-brains.org/.  Back to cited text no. 33
    
34.
Computer-Aided Diagnosis of Dementia based on Structural MRI Data. Alzheimer's Disease; 2019. Available from: https://www.caddementia.grand-challenge.org/download_data/. [Last accessed on 2019 Mar 09].  Back to cited text no. 34
    
35.
Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD – Public release of a multiple time point Alzheimer's MR imaging dataset. Neuroimage 2013;70:33-6.  Back to cited text no. 35
    
36.
Bloch BN, Ashali J, Jaffe CC. Data From BREAST-DIAGNOSIS. The Cancer Imaging Archive; 2015  Back to cited text no. 36
    
37.
David Newitt NH. Single Site Breast DCE-MRI Data and Segmentations from Patients Undergoing Neoadjuvant Chemotherapy. Cancer Imaging Archive; 2016.  Back to cited text no. 37
    
38.
Hylton NM, Gatsonis CA, Rosen MA, Lehman CD, Newitt DC, Partridge SC, et al. Neoadjuvant chemotherapy for breast cancer: Functional tumor volume by MR imaging predicts recurrence-free survival-results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology 2016;279:44-55.  Back to cited text no. 38
    
39.
David Newitt NH. On Behalf of The I-SPY 1 Network and ACRIN 6657 Trial Team. Multi-Center Breast DCE-MRI Data and Segmentations from Patients in the I-SPY 1/ACRIN 6657 Trials. The Cancer Imaging Archive; 2016.  Back to cited text no. 39
    
40.
Jansen S, Ileva L, Lu L, Van Dyke T. TCIA Mouse-Mammary Collection. The Cancer Imaging Archive; 2015.  Back to cited text no. 40
    
41.
Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, et al. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: A multicenter data analysis challenge. Transl Oncol 2014;7:153-66.  Back to cited text no. 41
    
42.
Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, et al. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 2015;50:195-204.  Back to cited text no. 42
    
43.
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318:2199-210.  Back to cited text no. 43
    
44.
Tripathi AS, Mathur A, Daga M, Kuse M, Au OC. 2-SiMDoM: A 2-Sieve Model for Detection of Mitosis in Multispectral Breast Cancer Imagery. International Conference on Image Processing; 2013. p. 611-5.  Back to cited text no. 44
    
45.
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.  Back to cited text no. 45
    
46.
Rubin EH, Storandt M, Miller JP, Kinscherf DA, Grant EA, Morris JC, et al. Aprospective study of cognitive function and onset of dementia in cognitively healthy elders. Arch Neurol 1998;55:395-401.  Back to cited text no. 46
    
47.
Morris JC. The clinical dementia rating: Current version and scoring rules. Neurology 1993;43:2412-4.  Back to cited text no. 47
    
48.
Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64:1032-9.  Back to cited text no. 48
    
49.
Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, et al. Aunified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: Reliability and validation against manual measurement of total intracranial volume. Neuroimage 2004;23:724-38.  Back to cited text no. 49
    
50.
OASIS Brains Datasets. Open Access Series of Imaging Studies; 2019. Available from: http://www.oasis-brains.org/. [Last accessed on 2019 Mar 09].  Back to cited text no. 50
    
51.
Amish Connectome Project. CRHD Amish Connectome Project; 2019. Available from: https://www.humanconnectome.org/study/amish-connectome-project. [Last accessed on 2019 Mar 09].  Back to cited text no. 51
    
52.
Alzheimer's Disease Connectome Project. CRHD Alzheimer's Disease Connectome Project; 2019. Available from: https://www.humanconnectome.org/study/alzheimers -disease-connectome-project. [Last accessed on 2019 Mar 09].  Back to cited text no. 52
    
53.
Standard HCP Demographics. CRHD Changes in Visual Cortical Connectivity Following Central Visual Field Loss. Standard HCP Demographics; 2019. Available from: https://www.humanconnectome.org/study/changes-visual-cortical-connectivity-following-central-visual-field-loss. [Last accessed on 2019 Mar 09].  Back to cited text no. 53
    
54.
Dimensional Connectomics of Anxious Misery Project. CRHD Dimensional Connectomics of Anxious Misery; 2019. Available from: https://www.humanconnectome.org/study/crhd-dimensional-connectomics-anxious-misery. [Last accessed on 2019 Mar 09].   Back to cited text no. 54
    
55.
Standard HCP Demographics, Along with Demographic Surveys, Medical and Seizure History. CRHD Epilepsy Connectome Project; 2019. Available from: https://www.humanconnectome.org/study/epilepsy-connectome-project. [Last accessed on 2019 Mar 09].  Back to cited text no. 55
    
56.
Prah MA, Stufflebeam SM, Paulson ES, Kalpathy-Cramer J, Gerstner ER, Batchelor TT, et al. Repeatability of standardized and normalized relative CBV in patients with newly diagnosed glioblastoma. AJNR Am J Neuroradiol 2015;36:1654-61.  Back to cited text no. 56
    
57.
Carass A, Roy S, Jog A, Cuzzocreo JL, Magrath E, Gherman A, et al. Longitudinal multiple sclerosis lesion segmentation data resource. Data Brief 2017;12:346-50.  Back to cited text no. 57
    
58.
Mendrik AM, Vincken KL, Kuijf HJ, Breeuwer M, Bouvy WH, de Bresser J, et al. MRBrainS challenge: Online evaluation framework for brain image segmentation in 3T MRI scans. Comput Intell Neurosci 2015;2015:813696.  Back to cited text no. 58
    
59.
Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat 2015;9:142.  Back to cited text no. 59
    
60.
Cardona A, Saalfeld S, Preibisch S, Schmid B, Cheng A, Pulokas J, et al. An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol 2010;8. pii: e1000502.  Back to cited text no. 60
    
61.
Dickson J, Drury H, Van Essen DC. 'The surface management system' (SuMS) database: A surface-based database to aid cortical surface reconstruction, visualization and analysis. Philos Trans R Soc Lond B Biol Sci 2001;356:1277-92.  Back to cited text no. 61
    
62.
Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, et al. The NKI-rockland sample: A Model for accelerating the pace of discovery science in psychiatry. Front Neurosci 2012;6:152.  Back to cited text no. 62
    
63.
Tian L, Wang J, Yan C, He Y. Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study. Neuroimage 2011;54:191-202.  Back to cited text no. 63
    
64.
Yan C, Gong G, Wang J, Wang D, Liu D, Zhu C, et al. Sex- and brain size-related small-world structural cortical networks in young adults: A DTI tractography study. Cereb Cortex 2011;21:449-58.  Back to cited text no. 64
    
65.
Arslan S, Ktena SI, Makropoulos A, Robinson EC, Rueckert D, Parisot S, et al. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 2018;170:5-30.  Back to cited text no. 65
    
66.
Richards JE, Xie W. Brains for all the ages: Structural neurodevelopment in infants and children from a life-span perspective. Adv Child Dev Behav 2015;48:1-52.  Back to cited text no. 66
    
67.
van Ginneken B, Armato SG 3rd, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, et al. Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study. Med Image Anal 2010;14:707-22.  Back to cited text no. 67
    
68.
Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P, et al. Interactive whole-heart segmentation in congenital heart disease. Med Image Comput Comput Assist Interv 2015;9351:80-8.  Back to cited text no. 68
    
69.
Wolterink JM, Leiner T, de Vos BD, Coatrieux JL, Kelm BM, Kondo S, et al. An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orcascore framework. Med Phys 2016;43:2361.  Back to cited text no. 69
    
70.
Jaeger S, Candemir S, Antani S, Wáng YX, Lu PX, Thoma G, et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg 2014;4:475-7.  Back to cited text no. 70
    
71.
Castillo E, Castillo R, Martinez J, Shenoy M, Guerrero T. Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 2010;55:305-27.  Back to cited text no. 71
    
72.
Castillo R, Castillo E, Fuentes D, Ahmad M, Wood AM, Ludwig MS, et al. Areference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive. Phys Med Biol 2013;58:2861-77.  Back to cited text no. 72
    
73.
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR; 2017. p. 2097-106.   Back to cited text no. 73
    
74.
BigNeuron; 2019. Available from: https://www.alleninstitute.org/bigneuron/about/. [Last accessed on 2019 Mar 09].  Back to cited text no. 74
    
75.
The Cancer Imaging Archive (TCIA) Public Access. DICOM Modality Abbreviations. The Cancer Imaging Archive; 2019. Available from: https://www.wiki.cancerimagingarchive.net/display/Public/DICOM+Modality+Abbreviations. [Last accessed on 2019 Mar 09].  Back to cited text no. 75
    
76.
Choyke P, Baris T, Pinto P, Merino M, Wood B. Data From PROSTATE-MRI. The Cancer Imaging Archive; 2016.  Back to cited text no. 76
    
77.
Jafari-Khouzani K, Emblem KE, Kalpathy-Cramer J, Bjørnerud A, Vangel MG, Gerstner ER, et al. Repeatability of cerebral perfusion using dynamic susceptibility contrast MRI in glioblastoma patients. Transl Oncol 2015;8:137-46.  Back to cited text no. 77
    
78.
Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Tempany C, et al. Acomparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magn Reson Imaging 2014;32:321-9.  Back to cited text no. 78
    
79.
Chao-Gan Y, Yu-Feng Z. DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 2010;4:13.  Back to cited text no. 79
    
80.
Johnson CD, Chen MH, Toledano AY, Heiken JP, Dachman A, Kuo MD, et al. Accuracy of CT colonography for detection of large adenomas and cancers. N Engl J Med 2008;359:1207-17.  Back to cited text no. 80
    
81.
Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, et al. DICOM for quantitative imaging biomarker development: A standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 2016;4:e2057.  Back to cited text no. 81
    
82.
Raudaschl PF, Zaffino P, Sharp GC, Spadea MF, Chen A, Dawant BM, et al. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015. Med Phys 2017;44:2020-36.  Back to cited text no. 82
    
83.
Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, et al. Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge. IEEE Trans Med Imaging 2011;30:1901-20.  Back to cited text no. 83
    
84.
Glocker B, Zikic D, Konukoglu E, Haynor DR, Criminisi A. Vertebrae localization in pathological spine CT via dense classification from sparse annotations. Med Image Comput Comput Assist Interv 2013;16:262-70.  Back to cited text no. 84
    
85.
Glocker B, Feulner J, Criminisi A, Haynor DR, Konukoglu E. Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. Med Image Comput Comput Assist Interv 2012;15:590-8.  Back to cited text no. 85
    
86.
Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, et al. Amulti-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph 2016;49:16-28.  Back to cited text no. 86
    
87.
Centers For Disease Control and Prevention; 2019. Available from: https://www.cdc.gov/cancer/dcpc/data/women.htm. [Last accessed on 2019 Mar 09].  Back to cited text no. 87
    
88.
World Health Organization. Breast Cancer: Prevention and Control. World Health Organization; 2019. Available from: http://www.who.int/cancer/detection/breastcancer/en/. [Last accessed on 2019 Mar 09].  Back to cited text no. 88
    
89.
Human Brain Project; 2019. Available from: http://www.humanbrainproject.eu/en/science/overview/. [Last accessed on 2019 Mar 09].  Back to cited text no. 89
    
90.
Standard HCP Demographics. CRHD Neural Disconnection & Errant Visual Perception in Psychotic Psychopathology; 2019. Available from: https://www.humanconnectome.org/study/crhd-neural-disconnection-errant-visual-perception-psychotic-psychopathology.  Back to cited text no. 90
    
91.
Jackson EF, Barboriak DP, Bidaut LM, Meyer CR. Magnetic resonance assessment of response to therapy: Tumor change measurement, truth data and error sources. Transl Oncol 2009;2:211-5.  Back to cited text no. 91
    
92.
Laboratory of Imaging Technologies; 2019. Available from: http://www.lit.fe.uni-lj.si/tools.php?lang=eng. [Last accessed on 2019 Mar 09].  Back to cited text no. 92
    
93.
Magnetic Resonance Imaging in Ms (MAGIMS); 2019. Available from: https://www.magnims.eu/. [Last accessed on 2019 Mar 09].  Back to cited text no. 93
    
94.
Pedano N, Flanders AE, Scarpace L, Mikkelsen T, Eschbacher JM, Hermes B, et al. Radiology Data from The Cancer Genome Atlas Low Grade Glioma [TCGA-LGG] Collection. The Cancer Imaging Archive; 2016.  Back to cited text no. 94
    
95.
Attention Deficit Hyperactivity Disorder. The ADHD-200 Sample; 2019. Available from: http://www.fcon_1000.projects.nitrc.org/indi/adhd200/index.html. [Last accessed on 2019 Mar 09].  Back to cited text no. 95
    
96.
Cancer Imaging Archive. National Lung Screening Trial. Cancer Imaging Archive; 2019. Available from: https://www.wiki.cancerimagingarchive.net/display/NLST/National +Lung+Screening+Trial-cf51362736104a4db320af2fb81a067b. [Last accessed on 2019 Mar 09].  Back to cited text no. 96
    
97.
LUNGx SPIE-AAPM-NCI Lung Nodule Classification Challenge; 2019. Available from: https://www.wiki.cancerimagingarchive.net/display/Public/LUNGx+SPIE-AAPM-NCI+Lung+Nodule+Classification+Challenge;jsessionid=047B7485951802AA601A946DD9D02943. [Last accessed on 2019 Mar 09].  Back to cited text no. 97
    
98.
LObe and Lung Analysis 2011 (LOLA11); 2019. Available from: https://www.lola11.grand-challenge.org/. [Last accessed on 2019 Mar 09].  Back to cited text no. 98
    
99.
Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, et al. Extraction of airways from CT (EXACT'09). IEEE Trans Med Imaging 2012;31:2093-107.  Back to cited text no. 99
    
100.
Image CLEE/LifeCLEF – Multimedia Retrieval in CLEF; 2019. Available from: https://www.imageclef.org/2017/tuberculosis. [Last accessed on 2019 Mar 09].  Back to cited text no. 100
    
101.
Communications Engineering Branch. Tuberculosis Chest X-ray Image Data Set; 2019. Available from: https://www.ceb.nlm.nih.gov/repositories/tuberculosis-chest-x-ray-image-data-sets/. [Last accessed on 2019 Mar 09].  Back to cited text no. 101
    
102.
Armato SG 3rd, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, et al. SPIE-AAPM-NCI lung nodule classification challenge dataset. Cancer Imaging Arch 2015. [doi.org/10.7937/K9/TCIA.2015.UZLSU3FL].  Back to cited text no. 102
    
103.
Kirk S, Lee Y, Kumar P, Filippini J, Albertina B, Watson M, et al. Radiology Data from The Cancer Genome Atlas Lung Squamous Cell Carcinoma [TCGA-LUSC] Collection. The Cancer Imaging Archive; 2016.  Back to cited text no. 103
    
104.
Centers for Disease Control and Prevention. Lung Cancer Screening; 2019. Available from: https://www.cdc.gov/cancer/lung/. [Last accessed on 2019 Mar 09].  Back to cited text no. 104
    
105.
Lung Carcinoma: Tumors of the Lungs. Merck Manual Professional Edition, Online Edition; Archived from the Original on 16 August, 2007. Available from: https://www.merckmanuals.com/professional. [Last accessed on 2019 Mar 09].  Back to cited text no. 105
    
106.
World Health Organization. Countries. World Health Organization; 2019. Available from: http://www.who.int/countries/en/. [Last accessed on 2019 Mar 09].  Back to cited text no. 106
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusions
References
Article Figures

 Article Access Statistics
    Viewed188    
    Printed20    
    Emailed0    
    PDF Downloaded49    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]