Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

A. Letyagin, B. Tuchinov, E. Amelina, E. N. Pavlovsky, S. K. Golushko, M. E. Amelin, D. A. Rzaev
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Abstract

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.”
神经肿瘤病变分割和分类技术中的人工智能
本研究致力于探讨基于人工智能(AI)和计算机视觉的神经肿瘤核磁共振成像图像诊断技术的现代开发方法的有效性。这种方法对于快速和有效的诊断分析是必要的,以实现个体化医疗的原则。材料和方法本文分析了选择人工智能技术对神经肿瘤核磁共振成像图像的分割和分类过程的有效性。人工智能在自己的注释数据库(SBT 数据集)上进行了训练,该数据库包含约 1000 个临床病例,这些病例基于联邦神经外科中心(俄罗斯联邦,新西伯利亚)术前磁共振成像研究的档案数据,涉及星形细胞瘤、胶质母细胞瘤、脑膜瘤、神经瘤患者以及体细胞肿瘤转移患者,并经过术后组织学和组织化学确认。结果与讨论在国际 BraTS 竞赛期间,对所开发技术的有效性和效率进行了测试,竞赛建议从竞赛组织者准备的神经肿瘤患者数据集中对病例进行分割和分类。最后得出结论。文章中提出的基于人工智能和计算机视觉原理开发诊断系统的方法论,在数十位世界顶尖专家的水平上显示出高效率,可用于开发具有 "医生助手 "功能的神经放射学诊断软硬件系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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