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| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 5
| Issue : 1 | Page : 13-21 |
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Statistical survey of open source medical image databases on the Internet
Hongkai Wang1, Xinlei Ma1, Haoyu Zhai1, Yuhao Liao1, Yi Wu2, Na Chen2, Shaoxiang Zhang2, Bin Zhang1
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 Publication | 29-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
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_1_19
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 2023 Mar 29];5:13-21. Available from: http://www.digitmedicine.com/text.asp?2019/5/1/13/259306 |
| Introduction | |  |
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 | | |
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 | | |
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 | | |
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 | | |
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.
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