kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-1-37-51kaz29-2498Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEАНСАМБЛЕВАЯ МОДЕЛЬ НА ОСНОВЕ ТРАНСФОРМЕРОВ ДЛЯ СЕГМЕНТАЦИИ ИШЕМИЧЕСКОГО ИНСУЛЬТА НА 3D КТTRANSFORMER BASED ENSEMBLE FOR ISCHEMIC STROKE SEGMENTATION ON 3D CT SCANShttps://orcid.org/0000-0001-8948-4205ЧерикбаеваЛ. Ш.CherikbayevaL. Ch.

PhD

г. Алматы

PhD

Almaty

cherikbayeva.lyailya@gmail.comhttps://orcid.org/0000-0002-5207-9764БериковВ. Б.BerikovV. B.

д.т.н., профессор

г. Новосибирск

Dr. Tech. Sc., Professor

Novosibirsk

berikov@math.nsc.ruhttps://orcid.org/0000-0003-4244-8121МелисЗ. М.MelisZ. M.

докторант

г. Алматы

PhD student

Almaty

melis.zarina98@gmail.comhttps://orcid.org/0000-0002-0425-6527ЕлеусиновА. И.YeleussinovA. I.

докторант

г. Алматы

PhD student

Almaty

armankaznu@mail.ruАдилжановаС. А.AdilzhanovaS. A.

PhD

г. Алматы

PhD

Almaty

asaltanat81@gmail.comhttps://orcid.org/0000-0003-1122-6614АтаниязоваА. С.AtaniyazovaA. S.

докторант

г. Алматы

PhD student

Almaty

aisulu.ataniyazova@gmail.comhttps://orcid.org/0000-0002-4255-5456ДайырбаеваЭ. Н.DaiyrbayevaE. N.

докторант

г. Алматы

PhD student

Almaty

nurbekkyzyelmira@gmail.comКазНУ им. аль-ФарабиКазахстанAl Farabi Kazakh National UniversityKazakhstanНовосибирский государственный университетКазахстанNovosibirsk State UniversityKazakhstanКазНИТУ им. К.И. СатпаеваКазахстанSatbayev UniversityKazakhstan2026290320262313751Copyright © Черикбаева Л.Ш., Бериков В.Б., Мелис З.М., Елеусинов А.И., Адилжанова С.А., Атаниязова А.С., Дайырбаева Э.Н., 20262026Черикбаева Л.Ш., Бериков В.Б., Мелис З.М., Елеусинов А.И., Адилжанова С.А., Атаниязова А.С., Дайырбаева Э.Н.Cherikbayeva L.C., Berikov V.B., Melis Z.M., Yeleussinov A.I., Adilzhanova S.A., Ataniyazova A.S., Daiyrbayeva E.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2498

Ишемический инсульт является одной из основных причин смертности и инвалидности. Точная сегментация пораженных областей на КТ-снимках головного мозга имеет решающее значение для своевременной диагностики и принятия клинических решений. В данной работе предлагается ансамблевая методика, основанная на объединении моделей трансформеров SE-UNETR и Swin UNETR с помощью взвешенного голосования. Для оценки качества использовался коэффициент Дайса, метрика, измеряющая степень пересечения предсказанных областей поражения с эталонной разметкой. В отличие от использования одиночных моделей, ансамблевые нейросетевые подходы обеспечивают более высокую надежность и точность сегментации за счет согласованного объединения предсказаний нескольких архитектур. Были использованы трехмерные КТ-снимки 98 пациентов с острым ишемическим инсультом, предоставленные Международным центром томографии Сибирского отделения Российской академии наук. Результаты подтвердили, что предложенный ансамбль демонстрирует более высокую производительность по сравнению с отдельными моделями. Среднее значение коэффициента Дайса составило 0.7983, что свидетельствует о высокой эффективности метода при сегментации ишемических очагов. Анализ показал, что ансамблевая методика позволяет более точно определять границы поражений на КТ-снимках головного мозга и снижает ошибки сегментации. Предложенный подход может быть применен не только для инсульта, но и для других патологий, требующих точного анализа медицинских изображений в автоматизированных системах диагностики.

Ischemic stroke is one of the leading causes of mortality and disability. Accurate segmentation of damaged regions in brain CT images is critical for timely diagnosis and clinical decision-making. In this study, an ensemble approach is proposed, combining SE-UNETR and Swin UNETR transformer models via weighted voting. The Dice coefficient was used for evaluation, measuring the overlap between predicted lesion regions and reference annotations. Unlike single-model approaches, ensemble neural network methods provide higher reliability and segmentation accuracy by integrating predictions from multiple architectures. Three-dimensional CT scans of 98 patients with acute ischemic stroke, provided by the International Tomography Center of the Siberian Branch of the Russian Academy of Sciences, were used. The results demonstrated that the proposed ensemble outperforms individual models. The average Dice coefficient was 0.7983, indicating the high effectiveness of the method in segmenting ischemic lesions. Analysis showed that the ensemble approach more accurately delineates lesion boundaries in brain CT images and reduces segmentation errors. The proposed method can be applied not only to stroke but also to other pathologies requiring precise medical image analysis in automated diagnostic systems.

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The authors declare that there are no conflicts of interest present.