Methodology and tools for creating training samples for artificial intelligence systems for recognizing lung cancer on CT images
- 作者: Kulberg N.S.1,2, Gusev M.A.1,3, Reshetnikov R.V.1,4, Elizarov A.B.1, Novik V.P.1, Prokudaylo S.B.1, Philippovich Y.N.3, Gobmolevsky V.A.1, Vladzymyrskyy A.V.1, Kamynina N.N.5, Morozov S.P.1
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隶属关系:
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
- Federal Research Center «Computer Science and Control» of Russian Academy of Sciences
- Moscow Polytechnic Uniersity
- Institute of Molecular Medicine, Sechenov First Moscow State Medical University
- Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
- 期: 卷 64, 编号 6 (2020)
- 页面: 343-350
- 栏目: PROBLEMS OF SOCIALLY SIGNIFICANT DISEASES
- ##submission.dateSubmitted##: 25.10.2024
- URL: https://hum-ecol.ru/0044-197X/article/view/637940
- DOI: https://doi.org/10.46563/0044-197X-2020-64-6-343-350
- ID: 637940
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Introduction. Medical imaging techniques can diagnose many diseases at the early stages of their development, improving the patient survival. Artificial intelligence (AI) systems, requiring the high-quality annotated and marked-up sets of medical images, are a suitable and promising means of improving the diagnostics’ quality.
The purpose of the study was to develop a methodology and software for creating AIS training sets.
Material and methods. We compared the main annotation methods’ performance and accuracy and based the information system on the most efficient method in both domains to develop an optimal approach. To markup objects of interest, we used the cluster model of lesions localization previously developed by the authors. We used C++ and Kotlin programming languages for software development.
Results. A structured annotation template with delivered a glossary of terms became the basis of the information system. The latter consists of three interacting modules, two of which are executed on a remote server’s capacities and one on a personal computer or mobile device of the end-user. The first module is a web service responsible for the workflow logic. The second module, a web server, is responsible for interacting with client applications. Its role is to identify users and manage the database and Picture Archiving and Communication System (PACS) connections. The front-end module is a web application with a graphical interface that assists the end-user in images’ markup and annotation.
Conclusions. An algorithmic basis and a software package have been created for annotation and markup of CT images. The resulting information system was used in a large-scale lung cancer screening project for the creation of medical imaging datasets.
作者简介
Nikolay Kulberg
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department; Federal Research Center «Computer Science and Control» of Russian Academy of Sciences
编辑信件的主要联系方式.
Email: kulberg@npcmr.ru
ORCID iD: 0000-0001-7046-7157
MD, Ph.D., head of the Department, Scientific and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, 109029, Russia.
e-mail: kulberg@npcmr.ru
Maxim Gusev
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department; Moscow Polytechnic Uniersity
Email: noemail@neicon.ru
ORCID iD: 0000-0001-8864-8722
俄罗斯联邦
Roman Reshetnikov
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department; Institute of Molecular Medicine, Sechenov First Moscow State Medical University
Email: noemail@neicon.ru
ORCID iD: 0000-0002-9661-0254
俄罗斯联邦
Alexey Elizarov
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0003-3786-4171
俄罗斯联邦
Vladimir Novik
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0002-6752-1375
俄罗斯联邦
Sergey Prokudaylo
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0003-0970-3645
俄罗斯联邦
Yuriy Philippovich
Moscow Polytechnic Uniersity
Email: noemail@neicon.ru
ORCID iD: 0000-0001-9419-2282
俄罗斯联邦
Victor Gobmolevsky
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0003-1816-1315
俄罗斯联邦
Anton Vladzymyrskyy
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0002-2990-7736
俄罗斯联邦
Natalya Kamynina
Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department
Email: noemail@neicon.ru
ORCID iD: 0000-0002-0925-5822
俄罗斯联邦
Sergey Morozov
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Email: noemail@neicon.ru
ORCID iD: 0000-0001-6545-6170
俄罗斯联邦
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