<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">bsuir</journal-id><journal-title-group><journal-title xml:lang="ru">Доклады БГУИР</journal-title><trans-title-group xml:lang="en"><trans-title>Doklady BGUIR</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1729-7648</issn><issn pub-type="epub">2708-0382</issn><publisher><publisher-name>БГУИР</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.35596/1729-7648-2026-24-3-77-84</article-id><article-id custom-type="elpub" pub-id-type="custom">bsuir-4378</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Статьи</subject></subj-group></article-categories><title-group><article-title>Алгоритм интеллектуального анализа КТ-изображений для построения 3D-модели</article-title><trans-title-group xml:lang="en"><trans-title>Intelligent CT Image Analysis Algorithm for 3D Model Construction</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Курочка</surname><given-names>К. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Kurachka</surname><given-names>K.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Гомель</p></bio><bio xml:lang="en"><p>Kurachka K., Cand. Sci. (Tech.), Associate Professor, Head of the Department of Information Technology</p><p>Gomel</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ванг</surname><given-names>Сюэмэй</given-names></name><name name-style="western" xml:lang="en"><surname>Wang</surname><given-names>Xuemei</given-names></name></name-alternatives><bio xml:lang="ru"><p>Гомель</p></bio><bio xml:lang="en"><p>Wang Xuemei, Postgraduate of the Department of Information Technology</p><p>246029, Gomel, Octiabria Ave., 48</p><p>Tel.: +375 23 232-95-65</p></bio><email xlink:type="simple">wxm_doc@qq.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Рен</surname><given-names>Хуанхай</given-names></name><name name-style="western" xml:lang="en"><surname>Ren</surname><given-names>Huanhai</given-names></name></name-alternatives><bio xml:lang="ru"><p>Гомель</p></bio><bio xml:lang="en"><p>Ren Huanhai, Postgraduate of the Department of Information Technology</p><p>Gomel</p></bio><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Гомельский государственный технический университет имени П. О. Сухого</institution></aff><aff xml:lang="en"><institution>Sukhoi State Technical University of Gomel (SSTUG)</institution></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>29</day><month>06</month><year>2026</year></pub-date><volume>24</volume><issue>3</issue><fpage>77</fpage><lpage>84</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Курочка К.С., Ванг С., Рен Х., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Курочка К.С., Ванг С., Рен Х.</copyright-holder><copyright-holder xml:lang="en">Kurachka K., Wang X., Ren H.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://doklady.bsuir.by/jour/article/view/4378">https://doklady.bsuir.by/jour/article/view/4378</self-uri><abstract><p>В статье представлены алгоритм и соответствующие программные средства, реализующие комплексный вычислительный конвейер на основе модели YOLOv8n-seg (компания Ultralytics) для автоматизированного анализа КТ-изображений поясничного отдела позвоночника человека, включая локализацию анатомических областей, сегментацию и количественные измерения позвонков, а также трехмерную визуализацию. При этом геометрические параметры (общая высота поясничного отдела и сегментарные углы) вычисляются непосредственно на основе сегментационных масок и конвертируются в физические единицы измерения с использованием разрешения изображения. В результате генерируется STL-файл, который может быть экспортирован в стороннее программное обеспечение (например, 3D Slicer) для реконструкции трехмерной поверхности. Критически важным аспектом разработанного подхода является акцент на количественной точности и воспроизводимости результатов, а не исключительно на визуализации: измерения, получаемые на основе масок, транслируют выходные данные модели в клинически интерпретируемые показатели. Это открывает возможности для последующих приложений, таких как скрининг, стратификация пациентов и лонгитюдное исследование.</p></abstract><trans-abstract xml:lang="en"><p>This paper presents an algorithm and associated software implementing a comprehensive computational pipeline based on the YOLOv8n-seg model (Ultralytics) for automated analysis of human lumbar spine CT images, including localization of anatomical regions, segmentation and quantitative measurements of vertebrae, and 3D visualization. Geometric parameters (total lumbar spine height and segmental angles) are calculated directly from segmentation masks and converted into physical units using the image resolution. This generates an STL file that can be exported to third-party software (e.g., 3D Slicer) for 3D surface reconstruction. A critical aspect of the developed approach is its emphasis on quantitative accuracy and reproducibility of results, rather than solely on visualization: measurements obtained from masks translate the model output into clinically interpretable metrics. This opens up opportunities for subsequent applications such as screening, patient stratification, and longitudinal studies.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>компьютерная томография</kwd><kwd>YOLOv8</kwd><kwd>поясничный отдел позвоночника</kwd><kwd>глубокое обучение</kwd><kwd>сегментация</kwd><kwd>геометрический анализ</kwd><kwd>трехмерная визуализация</kwd></kwd-group><kwd-group xml:lang="en"><kwd>computed tomography</kwd><kwd>YOLOv8</kwd><kwd>lumbar spine</kwd><kwd>deep learning</kwd><kwd>segmentation</kwd><kwd>geometric analysis</kwd><kwd>3D visualization</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Verheijen E. J. A., Chapman J. R., Lehr A. M., Schnake K. J., Vaccaro A. R., Oner F. C., et al. (2025) Artificial Intelligence for Segmentation and Classification in Lumbar Spinal Stenosis: An Overview of Current Methods. European Spine Journal. 34, 1146–1155. https://doi.org/10.1007/s00586-025-08672-9.</mixed-citation><mixed-citation xml:lang="en">Verheijen E. J. A., Chapman J. R., Lehr A. M., Schnake K. J., Vaccaro A. R., Oner F. C., et al. (2025) Artificial Intelligence for Segmentation and Classification in Lumbar Spinal Stenosis: An Overview of Current Methods. European Spine Journal. 34, 1146–1155. https://doi.org/10.1007/s00586-025-08672-9.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Masalitina N. N., Kurochka K. S., Tsitko E. L. (2019) The Mathematic Model of Lumbar Spine Osteochondrosis Treatment Decisions. Informatics. 16 (1), 24–35 (in Russian).</mixed-citation><mixed-citation xml:lang="en">Masalitina N. N., Kurochka K. S., Tsitko E. L. (2019) The Mathematic Model of Lumbar Spine Osteochondrosis Treatment Decisions. Informatics. 16 (1), 24–35 (in Russian).</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Constant C., Aubin C.-E., Kremers H. M., Garcia D. V. V., Wyles C. C., Rouzrokh P., et al. (2023) The Use of Deep Learning in Medical Imaging to Improve Spine Care: A Scoping Review of Current Literature and Clinical Applications. North American Spine Society Journal. 15. https://doi.org/10.1016/j.xnsj.2023.100236.</mixed-citation><mixed-citation xml:lang="en">Constant C., Aubin C.-E., Kremers H. M., Garcia D. V. V., Wyles C. C., Rouzrokh P., et al. (2023) The Use of Deep Learning in Medical Imaging to Improve Spine Care: A Scoping Review of Current Literature and Clinical Applications. North American Spine Society Journal. 15. https://doi.org/10.1016/j.xnsj.2023.100236.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Been E., Kalichman L. (2014) Lumbar Lordosis. The Spine Journal. 14 (1), 87–97. https://doi.org/10.1016/j.spinee.2013.07.464.</mixed-citation><mixed-citation xml:lang="en">Been E., Kalichman L. (2014) Lumbar Lordosis. The Spine Journal. 14 (1), 87–97. https://doi.org/10.1016/j.spinee.2013.07.464.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Been E., Li L., Hunter D. J., Kalichman L. (2011) Geometry of the Vertebral Bodies and the Intervertebral Discs in Lumbar Segments Adjacent to Spondylolysis and Spondylolisthesis: Pilot Study. European Spine Journal. 20 (7), 1159–1165. https://doi.org/10.1007/s00586-010-1660-y.</mixed-citation><mixed-citation xml:lang="en">Been E., Li L., Hunter D. J., Kalichman L. (2011) Geometry of the Vertebral Bodies and the Intervertebral Discs in Lumbar Segments Adjacent to Spondylolysis and Spondylolisthesis: Pilot Study. European Spine Journal. 20 (7), 1159–1165. https://doi.org/10.1007/s00586-010-1660-y.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Makirov S. K., Yuz A. A., Jahaf M. T., Nikulina A. A. (2015) Quantitative Evaluation of the Lumbosacral Sagittal Alignment in Degenerative Lumbar Spinal Stenosis. International Journal of Spine Surgery. 9. https://doi.org/10.14444/2068.</mixed-citation><mixed-citation xml:lang="en">Makirov S. K., Yuz A. A., Jahaf M. T., Nikulina A. A. (2015) Quantitative Evaluation of the Lumbosacral Sagittal Alignment in Degenerative Lumbar Spinal Stenosis. International Journal of Spine Surgery. 9. https://doi.org/10.14444/2068.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Boissiиre L., Bourghli A., Vital J.-M., Gille O., Obeid I. (2013) The Lumbar Lordosis Index: A New Ratio to Detect Spinal Malalignment with a Therapeutic Impact for Sagittal Balance Correction Decisions in Adult Scoliosis Surgery. European Spine Journal. 22 (6), 1339–1345. https://doi.org/10.1007/s00586-013-2711-y.</mixed-citation><mixed-citation xml:lang="en">Boissiиre L., Bourghli A., Vital J.-M., Gille O., Obeid I. (2013) The Lumbar Lordosis Index: A New Ratio to Detect Spinal Malalignment with a Therapeutic Impact for Sagittal Balance Correction Decisions in Adult Scoliosis Surgery. European Spine Journal. 22 (6), 1339–1345. https://doi.org/10.1007/s00586-013-2711-y.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Sadiqi S., Verlaan J. J., Lehr A. M., Chapman J. R., Dvorak M. F., Kandziora F., et al. (2017) Measurement of Kyphosis and Vertebral Body Height Loss in Traumatic Spine Fractures: An International Study. European Spine Journal. 26 (5), 1483–1491. https://doi.org/10.1007/s00586-016-4716-9.</mixed-citation><mixed-citation xml:lang="en">Sadiqi S., Verlaan J. J., Lehr A. M., Chapman J. R., Dvorak M. F., Kandziora F., et al. (2017) Measurement of Kyphosis and Vertebral Body Height Loss in Traumatic Spine Fractures: An International Study. European Spine Journal. 26 (5), 1483–1491. https://doi.org/10.1007/s00586-016-4716-9.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Kurochka K. S., Panarin K. A. (2018) Algorithm of Definition of Mutual Arrangement of L1–L5 Vertebrae on X-ray Images. Optical Memory and Neural Networks. 27 (3), 161–169.</mixed-citation><mixed-citation xml:lang="en">Kurochka K. S., Panarin K. A. (2018) Algorithm of Definition of Mutual Arrangement of L1–L5 Vertebrae on X-ray Images. Optical Memory and Neural Networks. 27 (3), 161–169.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Kurachka K., Wang X. (2025) Automation of Primary Diagnostics of Diseases of the Human Lumbar Spine Using Intelligent Analysis of CT Images. 2025 Open Semantic Technologies for Intelligent Systems (OSTIS-2025). 355–360.</mixed-citation><mixed-citation xml:lang="en">Kurachka K., Wang X. (2025) Automation of Primary Diagnostics of Diseases of the Human Lumbar Spine Using Intelligent Analysis of CT Images. 2025 Open Semantic Technologies for Intelligent Systems (OSTIS-2025). 355–360.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Kurachka K. S., Tsalka I. M. (2017) Vertebrae Detection in X-Ray Images Based on Deep Convolutional Neural Networks. 2017 IEEE 14th International Scientific Conference on Informatics (IEEE). 194–196.</mixed-citation><mixed-citation xml:lang="en">Kurachka K. S., Tsalka I. M. (2017) Vertebrae Detection in X-Ray Images Based on Deep Convolutional Neural Networks. 2017 IEEE 14th International Scientific Conference on Informatics (IEEE). 194–196.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
