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3 . 2021

Methodology of marking medical images with the function of crosscheck and intellectual segmentation

Abstract

Intelligent algorithms for medical image recognition need to be trained on a collection of annotated or tagged images. Image annotation and labeling underlie the medical interpretation of images for both clinical and research purposes. The article provides a methodology for organizing procedures for marking up medical images, including cross-marking by several specialists at once and attracting an expert's opinion in case of disagreement. The technique also considers the use of an image segmentation algorithm operating with graph cuts.

Keywords:medical images, image markup, Jaccard similarity coefficient

Funding. The study had no sponsor support.

Conflict of interest. The authors declare no conflict of interest.

Contribution. Development of intelligent algorithms in the methodology - Akhmetvaleev R.R.; development of a business model of the markup process - Shabanova K.I.; method design and evaluation of its use - Padukova A.A.; statement of the research problem - Lakman I.A.

For citation: Akhmetvaleev R.R., Shabanova K.I., Padukova A.A., Lakman I.A. Methodology of marking medical images with the function of cross-check and intellectual segmentation. ORGZDRAV: novosti, mneniya, obuchenie. Vestnik VSHOUZ [HEALTHCARE MANAGEMENT: News, Views, Education. Bulletin of VSHOUZ]. 2021; 7 (3): 62-9. DOI: https://doi.org/10.33029/2411-8621-2021-7-3-62-69 (in Russian)

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CHIEF EDITOR
CHIEF EDITOR
Guzel E. Ulumbekova
MD, MBA from Harvard University (Boston, USA), Head of the Graduate School of Healthcare Organization and Management (VSHOUZ)

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