ldtЛучевая диагностика и терапияDiagnostic radiology and radiotherapy2079-5343Baltic Medical Education Center10.22328/2079-5343-2025-16-1-7-18ldt-1074Research ArticleЛЕКЦИИ И ОБЗОРЫLECTURES AND REVIEWSВозможности искусственного интеллекта в сегментации и детекции изображений позвоночника на современном этапе развития: систематический обзорThe capabilities of artificial intelligence in segmentation and detection of spinal images at the current stage of development: a systematic reviewhttps://orcid.org/0009-0006-2726-1392ВасильевК. О.VasilyevK. O.

Васильев Константин Олегович — врач-рентгенолог отделения лучевой диагностики федерального; ассистент кафедры лучевой диагностики стоматологического факультета

630091, Новосибирск, ул. Фрунзе, д. 17; 630091, Новосибирск, ул. Красный проспект, д. 52

Konstantin O. Vasiliev — radiologist of the department of radiation diagnostics; Assistant at the Department of Radiation Diagnostics of the Dental Faculty

630091, Novosibirsk, st. Frunze, 17

vasiliev_ko@mail.ruhttps://orcid.org/0000-0001-8545-0024РерихВ. В.RerikhV. V.

Рерих Виктор Викторович — доктор медицинских наук, врач травматолог-ортопед, начальник научно-исследовательского отделения патологии позвоночника; профессор кафедры травматологии и ортопедии  

 630091, Новосибирск, ул. Фрунзе, д. 17; 630091, Новосибирск, ул. Красный проспект, д. 52

Viktor V. Rerikh — Dr. of Sci. (Med.), traumatologist-orthopedist, head of the research department of spine pathology; Professor of the Department of Traumatology and Orthopedics

630091, Novosibirsk, st. Frunze, 17;  630091, Novosibirsk, st. Krasny Prospekt, 52

rvv_nsk@mail.ruhttps://orcid.org/0000-0003-3438-819XУгольниковаЕ. А.UgolnikovaE. A.

Угольникова Екатерина Алексеевна  — младший научный сотрудник отделения биостатистики

630091, Новосибирск, ул. Фрунзе, д. 17

Ekaterina A. Ugolnikova — junior researcher at the Department of Biostatistics

630091, Novosibirsk, st. Frunze, 17

giekoolis@gmail.comНовосибирский научно-исследовательский институт травматологии и ортопедии имени Я.Л.Цивьяна; Новосибирский государственный медицинский университетРоссияNovosibirsk Research Institute of Traumatology and Orthopedics named after Ya. L. Tsivyan; Novosibirsk State Medical UniversityRussian FederationНовосибирский научно-исследовательский институт травматологии и ортопедии имени Я.Л.ЦивьянаРоссияNovosibirsk Research Institute of Traumatology and Orthopedics named after Ya. L. TsivyanRussian Federation202516042025161718Copyright © Васильев К.О., Рерих В.В., Угольникова Е.А., 20252025Васильев К.О., Рерих В.В., Угольникова Е.А.Vasilyev K.O., Rerikh V.V., Ugolnikova E.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://radiag.bmoc-spb.ru/jour/article/view/1074ВВЕДЕНИЕ

ВВЕДЕНИЕ: С самого начала теоретического обоснования создания искусственного интеллекта возникали идеи о создании электронного помощника для врача. Наиболее эффективными для решения такой задачи в  медицинской диагностике являются комплексные системы, основанные на одном из самых продвинутых методов искусственного интеллекта — нейросетях. Дизайн исследования: систематический обзор.

ЦЕЛЬ

ЦЕЛЬ: Определение возможностей нейросетей на современном этапе их развития в области таких задач, как сегментация и детекция изображений позвоночника.

МАТЕРИАЛЫ И  МЕТОДЫ

МАТЕРИАЛЫ И  МЕТОДЫ: При помощи протокола PRISMA был произведен поиск в  базе данных Pubmed за  период с января 2017 по 31 декабря 2022 г. при помощи ключевых слов, по которым производился поиск в аннотации или заголовке: («deep learning» OR «neural network» OR «artificial Intelligence») AND («spine») AND («detection» OR «segmentation»).

РЕЗУЛЬТАТЫ

РЕЗУЛЬТАТЫ: Для систематического обзора было отобрано 30 статей, в которых описывались такие функции нейросетей, как сегментация и детекция при анализе лучевых изображений позвоночника.

ОБСУЖДЕНИЕ

ОБСУЖДЕНИЕ: По результатам анализа источников литературы сделаны выводы о полезности применения искусственного интеллекта на современном этапе развития в вертебрологии.

ЗАКЛЮЧЕНИЕ

ЗАКЛЮЧЕНИЕ: Нейросети достаточно хорошо справляются с задачами сегментации и детекции в вертебрологии. При задачах сегментации присутствует однозначный лидер — U-Net и ее различные модификации. По детекции ведущими архитектура являются: SCRL, 3D FCN, CNN собственной разработки и комбинация сетей Mask R-CNN+ResNet101. Программное обеспечение, основанное на работах искусственного интеллекта, может помочь как рентгенологу, так и врачу-вертебрологу снизить нагрузку и упростить работу путем автоматизации и полуавтоматизации диагностического процесса.

INTRODUCTION

INTRODUCTION: From the very beginning of the theoretical substantiation of the creation of artificial intelligence, there were ideas about creating an electronic assistant for a doctor. The most effective for solving such a problem in medical diagnostics are complex systems based on one of the most advanced methods of artificial intelligence — neural networks. Study design: a systematic review.

OBJECTIVE

OBJECTIVE: Determining the capabilities of neural networks at the current stage of their development in the field of such tasks as segmentation and detection of spine images.

MATERIALS AND METHODS

MATERIALS AND METHODS: Using the PRISMA protocol, a search was performed in the Pubmed database for the period from January 2017 to December 31, 2022 using keywords «deep learning» OR «neural network» OR «artificial Intelligence», AND («spine») AND («detection» OR «segmentation»).

RESULTS

RESULTS: For the systematic review, 30 articles were selected that described such functions of neural networks as segmentation and detection in the analysis of spinal images. DISCUSSION: Based on the analysis of literature sources, conclusions were made about the usefulness of using artificial intelligence at the current stage of development in vertebrology.

CONCLUSION

CONCLUSION: Neural networks cope quite well with the tasks of segmentation and detection in vertebrology. In segmentation tasks, there is a clear leader — U-Net and its various modifications. In detection, the leading architectures are: SCRL, 3D FCN, CNN of our own design and a combination of Mask R-CNN+ResNet101 networks. Software based on the work of artificial intelligence can help both a radiologist and a vertebrologist reduce the load and simplify the work by automating and semi-automating the diagnostic process.

искусственный интеллектнейросетивертебрологияпозвоночниксегментацияобнаружениеartificial intelligenceneural networksvertebrologyspinesegmentationdetectionReferencesSarki R., Ahmed K., Wang H., Zhang Y., Wang K. Automated detection of COVID-19 through convolutional neural network using chest x-ray images // PloS ONE. 2022. Vol. 17, No. 1. e0262052. doi: 10.1371/journal.pone.0262052Sarki R., Ahmed K., Wang H., Zhang Y., Wang K. Automated detection of COVID-19 through convolutional neural network using chest x-ray images // PloS ONE. 2022. Vol. 17, No. 1. e0262052. doi: 10.1371/journal.pone.0262052Kann B.H., Hosny A., Aerts H. Artificial intelligence for clinical oncology // Cancer Cell. 2021. Vol. 39, No. 12. P. 916–927. doi: 10.1016/j.ccell.2021.04.002.Kann B.H., Hosny A., Aerts H. Artificial intelligence for clinical oncology // Cancer Cell. 2021. Vol. 39, No. 12. P. 916–927. doi: 10.1016/j.ccell.2021.04.002.Gerussi A., Scaravaglio M., Cristoferi L. et al. Artificial intelligence for precision medicine in autoimmune liver disease // Front Immunol. 2022. Vol. 13, No. 11. P. 966329. doi: 10.3389/fimmu.2022.966329.Gerussi A., Scaravaglio M., Cristoferi L. et al. Artificial intelligence for precision medicine in autoimmune liver disease // Front Immunol. 2022. Vol. 13, No. 11. P. 966329. doi: 10.3389/fimmu.2022.966329.Kröner P.T., Engels M.M., Glicksberg B.S. et al. Artificial intelligence in gastroenterology: A state-of-the-art review // World J. Gastroenterol. 2021. Vol. 40, No. 27. P. 6794–6824. doi: 10.3748/wjg.v27.i40.6794.Kröner P.T., Engels M.M., Glicksberg B.S. et al. Artificial intelligence in gastroenterology: A state-of-the-art review // World J. Gastroenterol. 2021. Vol. 40, No. 27. P. 6794–6824. doi: 10.3748/wjg.v27.i40.6794.Tricco A.C., Lillie E., Zarin W., O’Brien K.K., Colquhoun H., Levac. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation // Annals of Internal Medicine. 2018. Vol. 169, No. 7. P. 467–473. doi: 10.7326/M18-0850.Tricco A.C., Lillie E., Zarin W., O’Brien K.K., Colquhoun H., Levac. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation // Annals of Internal Medicine. 2018. Vol. 169, No. 7. P. 467–473. doi: 10.7326/M18-0850.Vrtovec T., Ibragimov B. Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation // European Spine Journal. 2022. Vol. 31, No. 8. P. 2031–2045. doi: 10.1007/s00586-022-07155-5.Vrtovec T., Ibragimov B. Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation // European Spine Journal. 2022. Vol. 31, No. 8. P. 2031–2045. doi: 10.1007/s00586-022-07155-5.Qu B., Cao J., Qian C. et al. Current development and prospects of deep learning in spine image analysis: a literature review // Quant. Imaging Med. Surg. 2022. Vol. 12. P. 3454–3479. doi: 10.21037/qims-21-939.Qu B., Cao J., Qian C. et al. Current development and prospects of deep learning in spine image analysis: a literature review // Quant. Imaging Med. Surg. 2022. Vol. 12. P. 3454–3479. doi: 10.21037/qims-21-939.Lundervold A. S. An overview of deep learning in medical imaging focusing on MRI // Zeitschrift für Medizinische Physik. 2019. Vol. 29, No. 2. P. 102–127. doi: 10.1016/j.zemedi.2018.11.002.Lundervold A. S. An overview of deep learning in medical imaging focusing on MRI // Zeitschrift für Medizinische Physik. 2019. Vol. 29, No. 2. P. 102–127. doi: 10.1016/j.zemedi.2018.11.002.Goedmakers C.M.W., Pereboom L.M., Schoones J.W., de Leeuw den Bouter M.L, Remis R.F. et al. Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods // Brain & Spine. 2022. Vol. 2, No. 14. P. 101666. doi: 10.1016/j.bas.2022.101666.Goedmakers C.M.W., Pereboom L.M., Schoones J.W., de Leeuw den Bouter M.L, Remis R.F. et al. Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods // Brain & Spine. 2022. Vol. 2, No. 14. P. 101666. doi: 10.1016/j.bas.2022.101666.Wang R., Lei T., Cui R., Zhang B., Meng H., Nandi A. Medical image segmentation using deep learning: A survey // IET Image Processing. 2022. Vol. 16, No. 5. P. 1243–1267. doi: 10.1049/ipr2.12419Wang R., Lei T., Cui R., Zhang B., Meng H., Nandi A. Medical image segmentation using deep learning: A survey // IET Image Processing. 2022. Vol. 16, No. 5. P. 1243–1267. doi: 10.1049/ipr2.12419Ashraf R., Habib M.A., Akram M. et al. Deep convolution neural network for big data medical image classification // IEEE Access. 2020. Vol. 8. P. 105659–105670. doi: 10.1109/ACCESS.2020.2998808.Ashraf R., Habib M.A., Akram M. et al. Deep convolution neural network for big data medical image classification // IEEE Access. 2020. Vol. 8. P. 105659–105670. doi: 10.1109/ACCESS.2020.2998808.Далечина А.В., Беляев М.Г., Тюрина А.Н., Золотова С.В., Пронин И.Н., Голанов А.В. Методы машинного обучения в сегментации глиом для планирования стереотаксической лучевой терапии // Лучевая диагностика и терапия. 2019. № 2. С. 24–31. doi: 10.22328/2079-5343-2019-10-2-24-31.Dalechina A.V., Beljaev M.G., Tjurina A.N., Zolotova S.V., Pronin I.N., Golanov A.V. Machine learning methods in glioma segmentation for planning stereotactic radiotherapy. Diagnostic radiology and radiotherapy, 2019, No. 2, рр. 24–31 (In Russ.) doi: 10.22328/2079-5343-2019-10-2-24-31.Litjens G., Kooi T., Bejnordi B.E., Setio A.A.A., Ciompi F. et al. A survey on deep learning in medical image analysis // Medical image analysis. 2017. Vol. 42. P. 60– 88. doi: 10.1016/j.media.2017.07.005.Litjens G., Kooi T., Bejnordi B.E., Setio A.A.A., Ciompi F. et al. A survey on deep learning in medical image analysis // Medical image analysis. 2017. Vol. 42. P. 60– 88. doi: 10.1016/j.media.2017.07.005.Al Arif S. M. R., Knapp K., Slabaugh G. Fully automatic cervical vertebrae segmentation framework for X-ray images // Computer methods and programs in biomedicine. 2018. Vol. 157. P. 95–111. doi: 10.1016/j.cmpb.2018.01.006.Al Arif S. M. R., Knapp K., Slabaugh G. Fully automatic cervical vertebrae segmentation framework for X-ray images // Computer methods and programs in biomedicine. 2018. Vol. 157. P. 95–111. doi: 10.1016/j.cmpb.2018.01.006.Kim S., Bae W.C., Masuda K., Chung C.B., Hwang D. Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net // Applied sciences. 2018. Vol. 8, No. 9. P. 1656. doi: 10.3390/app8091656.Kim S., Bae W.C., Masuda K., Chung C.B., Hwang D. Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net // Applied sciences. 2018. Vol. 8, No. 9. P. 1656. doi: 10.3390/app8091656.Fan G., Liu H., Wu Z. et al. Deep learning–based automatic segmentation of lumbosacral nerves on CT for spinal Intervention: a translational Study // American Journal of Neuroradiology. 2019. Vol. 40, No. 6. P. 1074–1081. doi: 10.3174/ajnr.A6070.Fan G., Liu H., Wu Z. et al. Deep learning–based automatic segmentation of lumbosacral nerves on CT for spinal Intervention: a translational Study // American Journal of Neuroradiology. 2019. Vol. 40, No. 6. P. 1074–1081. doi: 10.3174/ajnr.A6070.Huang J., Shen H., Wu J. et al. Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images // The Spine Journal. 2020. Vol. 20, No. 4. P. 590–599. doi: 10.1016/j.spinee.2019.11.010.Huang J., Shen H., Wu J. et al. Spine Explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images // The Spine Journal. 2020. Vol. 20, No. 4. P. 590–599. doi: 10.1016/j.spinee.2019.11.010.Kolařík M., Burget R., Uher V., Říha K., Dutta M.K. Optimized high resolution 3D dense-U-Net network for brain and spine segmentation // Applied Sciences. 2019. Vol. 9, No. 3. P. 404. doi: 10.3390/app9030404.Kolařík M., Burget R., Uher V., Říha K., Dutta M.K. Optimized high resolution 3D dense-U-Net network for brain and spine segmentation // Applied Sciences. 2019. Vol. 9, No. 3. P. 404. doi: 10.3390/app9030404.Rehman F., Ali Shah F.I., Riaz N., Gilani S.O. A robust scheme of vertebrae segmentation for medical diagnosis // IEEE Access. 2019. Vol. 7. P. 120387–120398. doi: 10.1109/ACCESS.2019.2936492.Rehman F., Ali Shah F.I., Riaz N., Gilani S.O. A robust scheme of vertebrae segmentation for medical diagnosis // IEEE Access. 2019. Vol. 7. P. 120387–120398. doi: 10.1109/ACCESS.2019.2936492.Fang Y., Li W., Chen X. et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks // European Radiology. 2021. Vol. 31, No. 4. P. 1831–1842. doi: 10.1007/s00330-020-07312-8.Fang Y., Li W., Chen X. et al. Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks // European Radiology. 2021. Vol. 31, No. 4. P. 1831–1842. doi: 10.1007/s00330-020-07312-8.Zhang Q., Du Y., Wei Z., Liu H., Yang X., Zhao D. Spine medical image segmentation based on deep learning // Journal of Healthcare Engineering. 2021. Vol. 2021. P. 1917946. doi: 10.1155/2021/1917946.Zhang Q., Du Y., Wei Z., Liu H., Yang X., Zhao D. Spine medical image segmentation based on deep learning // Journal of Healthcare Engineering. 2021. Vol. 2021. P. 1917946. doi: 10.1155/2021/1917946.Rak M., Steffen J., Meyer A., Hansen C., Tönnies K.D. Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI // Computer Methods and Programs in Biomedicine. 2019. Vol. 177. P. 47–56. doi: 10.1016/j.cmpb.2019.05.003.Rak M., Steffen J., Meyer A., Hansen C., Tönnies K.D. Combining convolutional neural networks and star convex cuts for fast whole spine vertebra segmentation in MRI // Computer Methods and Programs in Biomedicine. 2019. Vol. 177. P. 47–56. doi: 10.1016/j.cmpb.2019.05.003.Masood R. F., Taj I. A., Khan M. A., Qureshi M. A., Hassan T. Deep learning based vertebral body segmentation with extraction of spinal measurements and disorder disease classification // Biomedical Signal Processing and Control. 2022. Vol. 71. P. 103230. doi: 10.1016/j.bspc.2021.103230.Masood R. F., Taj I. A., Khan M. A., Qureshi M. A., Hassan T. Deep learning based vertebral body segmentation with extraction of spinal measurements and disorder disease classification // Biomedical Signal Processing and Control. 2022. Vol. 71. P. 103230. doi: 10.1016/j.bspc.2021.103230.Lavdas I., Glocker B., Kamnitsas K. et al. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CF s), convolutional neural networks (CNN s), and a multi‐atlas (MA) approach // Medical physics. 2017. Vol. 44, No. 10. P. 5210–5220. doi: 10.1002/mp.12492.Lavdas I., Glocker B., Kamnitsas K. et al. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CF s), convolutional neural networks (CNN s), and a multi‐atlas (MA) approach // Medical physics. 2017. Vol. 44, No. 10. P. 5210–5220. doi: 10.1002/mp.12492.Han Z., Wei B., Mercado A., Leung S., Li S. Spine-GAN: Semantic segmentation of multiple spinal structures // Medical image analysis. 2018. Vol. 50. P. 23–35. doi: 10.1016/j.media.2018.08.005.Han Z., Wei B., Mercado A., Leung S., Li S. Spine-GAN: Semantic segmentation of multiple spinal structures // Medical image analysis. 2018. Vol. 50. P. 23–35. doi: 10.1016/j.media.2018.08.005.Li X., Dou Q., Chen H. et al. 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images // Medical image analysis. 2018. Vol. 45. P. 41–54. doi: 10.1016/j.media.2018.01.004.Li X., Dou Q., Chen H. et al. 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images // Medical image analysis. 2018. Vol. 45. P. 41–54. doi: 10.1016/j.media.2018.01.004.Das P., Pal C., Acharyya A., Chakrabarti A., Basu S. Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images // Computer Methods and Programs in Biomedicine. 2021. Vol. 205. P. 106074. doi: 10.1016/j.cmpb.2021.106074.Das P., Pal C., Acharyya A., Chakrabarti A., Basu S. Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images // Computer Methods and Programs in Biomedicine. 2021. Vol. 205. P. 106074. doi: 10.1016/j.cmpb.2021.106074.Huo X., Cui G., Tan J., Shao K. Automatic measurement of axial vertebral rotation in 3D vertebral models // Biomedical Physics and Engineering Express. 2021. Vol. 7, No. 6. P. 065034. doi: 10.1088/2057-1976/ac2c55.Huo X., Cui G., Tan J., Shao K. Automatic measurement of axial vertebral rotation in 3D vertebral models // Biomedical Physics and Engineering Express. 2021. Vol. 7, No. 6. P. 065034. doi: 10.1088/2057-1976/ac2c55.Tran V.L., Lin H.Y., Liu H.W. MBNet: A multi-task deep neural network for semantic segmentation and lumbar vertebra inspection on X-ray images // Proceedings of the Asian Conference on Computer Vision. 2020. doi: 10.1007/978-3-030-69541-5_38.Tran V.L., Lin H.Y., Liu H.W. MBNet: A multi-task deep neural network for semantic segmentation and lumbar vertebra inspection on X-ray images // Proceedings of the Asian Conference on Computer Vision. 2020. doi: 10.1007/978-3-030-69541-5_38.Zhang Q., Du Y., Wei Z. et al. Spine medical image segmentation based on deep learning // Journal of Healthcare Engineering. 2021. Vol. 2021. P. 1917946. doi: 10.1155/2021/1917946.Zhang Q., Du Y., Wei Z. et al. Spine medical image segmentation based on deep learning // Journal of Healthcare Engineering. 2021. Vol. 2021. P. 1917946. doi: 10.1155/2021/1917946.Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation // Medical Image Computing and Computer-Assisted Intervention. 2015. P. 234–241. doi: 10.1007/978-3-319-24574-4_28.Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation // Medical Image Computing and Computer-Assisted Intervention. 2015. P. 234–241. doi: 10.1007/978-3-319-24574-4_28.Абдуллина К.М., Спивак А.И. Применение нейронной сети архитектуры U-Net для задачи локализации нарушений целостности цифровых изображений // Научно-технический вестник информационных технологий, механики и оптики. 2020. Т. 20, № 3. С. 425–431. doi: 10.17586/2226-1494-2020-20-3-425-431.Abdullina K.M., Spivak A.I. Application of a neural network of the U-Net architecture for the task of localizing violations of the integrity of digital images. Scientific and technical bulletin of information technologies, mechanics and optics, 2020, Vol. 20, No. 3, рр. 425–431 (In Russ.). doi: 10.17586/2226-1494-2020-20-3-425-431.Хамад Ю.А., Симонов К.В., Кенц А.С. Алгоритмы сегментации и распознавания объектов на медицинских изображениях на основе шиарлет-преобразования и нейронных сетей // Информатизация и связь. 2020. № 2. С. 35–45. doi: 10.34219/2078-8320-2020-11-2-35-45.Hamad Yu.A., Simonov K.V., Kenc A.S. Algorithms for segmentation and recognition of objects in medical images based on shearlet transformation and neural networks. Informatization and communication, 2020, No. 2, рр. 35–45 (In Russ.). doi: 10.34219/2078-8320-2020-11-2-35-45.Charng J., Xiao D., Mehdizadeh M. et al. Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease // Scientific Reports. 2020. Vol. 10, No. 1. P. 16491. doi: 10.1038/s41598-020-73339-y.Charng J., Xiao D., Mehdizadeh M. et al. Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease // Scientific Reports. 2020. Vol. 10, No. 1. P. 16491. doi: 10.1038/s41598-020-73339-y.Sezer A. Mask Region-Based Convolutional Neural Network segmentation of the humerus and scapula from proton density-weighted axial shoulder magnetic resonance images // Jt. Dis. Relat. Surg. 2023. Vol. 20, No. 34. P. 583–589. doi: 10.52312/jdrs.2023.1291.Sezer A. Mask Region-Based Convolutional Neural Network segmentation of the humerus and scapula from proton density-weighted axial shoulder magnetic resonance images // Jt. Dis. Relat. Surg. 2023. Vol. 20, No. 34. P. 583–589. doi: 10.52312/jdrs.2023.1291.Heller N., Athianathen N., Kalapara A. et al. The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes // arXiv preprint. arXiv: 201904.00445.2019.Heller N., Athianathen N., Kalapara A. et al. The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes // arXiv preprint. arXiv: 201904.00445.2019.Zhao S., Wu X., Chen B., Li S. Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework // Medical Image Analysis. 2021. Vol. 67. P. 101826. doi: 10.1016/j.media.2020.101826.Zhao S., Wu X., Chen B., Li S. Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework // Medical Image Analysis. 2021. Vol. 67. P. 101826. doi: 10.1016/j.media.2020.101826.Hallinan J.T.P.D., Zhu L., Yang K. et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI // Radiology. 2021. Vol. 300, No. 1. P. 130–138. doi: 10.1148/radiol.2021204289.Hallinan J.T.P.D., Zhu L., Yang K. et al. Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI // Radiology. 2021. Vol. 300, No. 1. P. 130–138. doi: 10.1148/radiol.2021204289.Huang Y., Uneri A., Jones C.K. et al. 3D vertebrae labeling in spine CT an accurate, memory-efficient (Ortho2D) framework // Physics in Medicine & Biology. 2021. Vol. 66, No. 12. P. 125020. doi: 10.1088/1361-6560/ac07c7.Huang Y., Uneri A., Jones C.K. et al. 3D vertebrae labeling in spine CT an accurate, memory-efficient (Ortho2D) framework // Physics in Medicine & Biology. 2021. Vol. 66, No. 12. P. 125020. doi: 10.1088/1361-6560/ac07c7.Forsberg D., Sjöblom E., Sunshine J.L. Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data // Journal of Digital Imaging. 2017. Vol. 30. P. 406–412. doi: 10.1007/s10278-017-9945-x.Forsberg D., Sjöblom E., Sunshine J.L. Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data // Journal of Digital Imaging. 2017. Vol. 30. P. 406–412. doi: 10.1007/s10278-017-9945-x.Kurochka K.S., Tsalka I.M. Vertebrae detection in X-ray images based on deep convolutional neural networks // 2017 IEEE 14th International Scientific Conference on Informatics. IEEE. 2017. P. 194–196.Kurochka K.S., Tsalka I.M. Vertebrae detection in X-ray images based on deep convolutional neural networks // 2017 IEEE 14th International Scientific Conference on Informatics. IEEE. 2017. P. 194–196.Yang D., Xiong T., Xu D., Huang Q., Liu D., Zhou S.K. et al. Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization // International Conference on Information Processing in Medical Imaging. 2017. P. 633–644. doi: 10.1007/978-3-319-59050-9_50.Yang D., Xiong T., Xu D., Huang Q., Liu D., Zhou S.K. et al. Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization // International Conference on Information Processing in Medical Imaging. 2017. P. 633–644. doi: 10.1007/978-3-319-59050-9_50.Liao H., Mesfin A., Luo J. Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long Range Contextual Information // IEEE transactions on medical imaging. 2018. Vol. 37. P. 1266–1275. doi: 10.1109/TMI.2018.2798293Liao H., Mesfin A., Luo J. Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long Range Contextual Information // IEEE transactions on medical imaging. 2018. Vol. 37. P. 1266–1275. doi: 10.1109/TMI.2018.2798293Jakubicek R., Chmelik J., Ourednicek P., Jan J. Deep-learning-based fully automatic spine centerline detection in CT data // 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. P. 2407–2410. doi: 10.1109/EMBC.2019.8856528.Jakubicek R., Chmelik J., Ourednicek P., Jan J. Deep-learning-based fully automatic spine centerline detection in CT data // 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019. P. 2407–2410. doi: 10.1109/EMBC.2019.8856528.Zhou Y., Liu Y., Chen Q., Gu G., Sui X. Automatic lumbar MRI detection and identification based on deep learning // Journal of digital imaging. 2019. Vol. 32, No. 3. P. 513–520. doi: 10.1007/s10278-018-0130-7.Zhou Y., Liu Y., Chen Q., Gu G., Sui X. Automatic lumbar MRI detection and identification based on deep learning // Journal of digital imaging. 2019. Vol. 32, No. 3. P. 513–520. doi: 10.1007/s10278-018-0130-7.Chen Y., Gao Y., Li K., Zhao L., Zhao J. Vertebrae identification and localization utilizing fully convolutional networks and a hidden Markov model // IEEE Transactions on Medical Imaging. 2019. Vol. 39, No. 2. P. 387–399. doi: 10.1109/TMI.2019.2927289.Chen Y., Gao Y., Li K., Zhao L., Zhao J. Vertebrae identification and localization utilizing fully convolutional networks and a hidden Markov model // IEEE Transactions on Medical Imaging. 2019. Vol. 39, No. 2. P. 387–399. doi: 10.1109/TMI.2019.2927289.Roggen T., Bobic M., Givenchi N., Scheib S.G. Deep Learning model for markerless tracking in spinal SBRT // Physica Medica. 2020. Vol. 74. P. 66–73. doi: 10.1016/j.ejmp.2020.04.029.Roggen T., Bobic M., Givenchi N., Scheib S.G. Deep Learning model for markerless tracking in spinal SBRT // Physica Medica. 2020. Vol. 74. P. 66–73. doi: 10.1016/j.ejmp.2020.04.029.Zhang D., Chen B., Li S. Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy // Medical Image Analysis. 2021. Vol. 67. P. 101861. doi: 10.1016/j.media.2020.101861.Zhang D., Chen B., Li S. Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy // Medical Image Analysis. 2021. Vol. 67. P. 101861. doi: 10.1016/j.media.2020.101861.Kim K.C., Cho H.C., Jang T.J., Choi J.M., Seo J.K. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation // Computer Methods and Programs in Biomedicine. 2021. Vol. 200. P. 105833. doi: 10.1016/j.cmpb.2020.105833.Kim K.C., Cho H.C., Jang T.J., Choi J.M., Seo J.K. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation // Computer Methods and Programs in Biomedicine. 2021. Vol. 200. P. 105833. doi: 10.1016/j.cmpb.2020.105833.Wang Yu., Huang L., Wu M. et al. Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays // Computers in Biology and Medicine. 2022. Vol. 146. P. 105576. doi: 10.1016/j.compbiomed.2022.105576.Wang Yu., Huang L., Wu M. et al. Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays // Computers in Biology and Medicine. 2022. Vol. 146. P. 105576. doi: 10.1016/j.compbiomed.2022.105576.

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