Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions
The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis...
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| Format: | Article |
| Language: | Russian |
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Russian Academy of Sciences, Siberian Branch Publishing House
2024-03-01
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| Series: | Сибирский научный медицинский журнал |
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| Online Access: | https://sibmed.elpub.ru/jour/article/view/1387 |
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| author | A. Yu. Letyagin B. N. Tuchinov E. V. Amelina E. N. Pavlovsky S. K. Golushko M. E. Amelin D. A. Rzaev |
| author_facet | A. Yu. Letyagin B. N. Tuchinov E. V. Amelina E. N. Pavlovsky S. K. Golushko M. E. Amelin D. A. Rzaev |
| author_sort | A. Yu. Letyagin |
| collection | DOAJ |
| description | The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.” |
| format | Article |
| id | doaj-art-cb0cb8918dc24e1a836d25baf2979f89 |
| institution | Matheson Library |
| issn | 2410-2512 2410-2520 |
| language | Russian |
| publishDate | 2024-03-01 |
| publisher | Russian Academy of Sciences, Siberian Branch Publishing House |
| record_format | Article |
| series | Сибирский научный медицинский журнал |
| spelling | doaj-art-cb0cb8918dc24e1a836d25baf2979f892025-07-15T11:26:38ZrusRussian Academy of Sciences, Siberian Branch Publishing HouseСибирский научный медицинский журнал2410-25122410-25202024-03-01441323810.18699/SSMJ20240104587Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesionsA. Yu. Letyagin0B. N. Tuchinov1E. V. Amelina2E. N. Pavlovsky3S. K. Golushko4M. E. Amelin5D. A. Rzaev6Novosibirsk State University; Research Institute of Clinical and Experimental Lymphology – Branch of the Institute of Cytology and Genetics of SB RASNovosibirsk State UniversityNovosibirsk State UniversityNovosibirsk State UniversityNovosibirsk State UniversityNovosibirsk State University; Federal Neurosurgical Center of the Ministry of Health of RussiaFederal Neurosurgical Center of the Ministry of Health of RussiaThe study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.”https://sibmed.elpub.ru/jour/article/view/1387mrineuro-oncologyartificial intelligencetumor segmentationclassification of brain tumors |
| spellingShingle | A. Yu. Letyagin B. N. Tuchinov E. V. Amelina E. N. Pavlovsky S. K. Golushko M. E. Amelin D. A. Rzaev Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions Сибирский научный медицинский журнал mri neuro-oncology artificial intelligence tumor segmentation classification of brain tumors |
| title | Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions |
| title_full | Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions |
| title_fullStr | Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions |
| title_full_unstemmed | Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions |
| title_short | Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions |
| title_sort | artificial intelligence in technologies for segmentation and classification of neuro oncological lesions |
| topic | mri neuro-oncology artificial intelligence tumor segmentation classification of brain tumors |
| url | https://sibmed.elpub.ru/jour/article/view/1387 |
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