Application of a computer vision algorithm to identify foci of demyelination in multiple sclerosis on MRI images

B. Tuchinov, V. Suvorov, K. O. Motorin, E. N. Pavlovsky, L. M. Vasilkiv, Y. Stankevich, A. A. Tulupov
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Abstract

The research was aimed at analyzing modern algorithms for diagnosing lesions in multiple sclerosis on MRI images. Multiple sclerosis is a severe disease of the central nervous system and ranks first among the causes of disability in patients of young working age. In connection with the development of computer vision and machine learning technologies, the relevance of using these technologies for medical diagnostics is growing. Such approaches are necessary for the effective development and implementation of diagnostic systems using artificial intelligence. Modern algorithms and models for lesion segmentation were selected and implemented. Material and methods. The paper presents CV features of diagnosing multiple sclerosis on MRI images, existing data sets: ISBI-2015, MSSEG-2016, MSSEG-2021; existing algorithms and models for lesion segmentation: U-Net, nnU-Net, TransUnet, TransBTS, UNETR, Swin UNETR. Results and discussion. The architectures and models of nnU-Net, UNETR, Swin UNETR were trained and compared at ISBI2015 with various parameters and loss functions. Four MRI sequences were used: T2-WI, T2-FLAIR, PD, MPRAGE. Lesion segmentation was approved by certified experienced neuroradiologists. Conclusions. The approaches described in the paper including data processing, model training, and results analysis, focused on the selection and development of high-quality computer vision algorithms for identifying multiple sclerosis lesions in MRI images. Identification and segmentation of demyelination foci is a necessary step for diagnosing the disease, as well as for calculating and interpreting more meaningful indicators of disease severity and progression.
应用计算机视觉算法在核磁共振成像上识别多发性硬化症的脱髓鞘病灶
这项研究旨在分析在核磁共振成像图像上诊断多发性硬化病变的现代算法。多发性硬化症是一种严重的中枢神经系统疾病,在年轻工作年龄段患者的致残原因中排名第一。随着计算机视觉和机器学习技术的发展,将这些技术用于医学诊断的相关性也在不断提高。这些方法对于有效开发和实施使用人工智能的诊断系统十分必要。我们选择并实施了用于病灶分割的现代算法和模型。材料和方法。本文介绍了在核磁共振成像图像上诊断多发性硬化症的 CV 特征、现有数据集:ISBI-2015、MSSEG-2016、MSSEG-2021;病灶分割的现有算法和模型:U-Net、nnU-Net、TransUnet、TransBTS、UNETR、Swin UNETR。结果与讨论。在 ISBI2015 上,对 nnU-Net、UNETR、Swin UNETR 的架构和模型进行了训练,并使用不同的参数和损失函数进行了比较。使用了四种 MRI 序列:T2-Wi、T2-Flair、PD、Mprage。病灶分割得到了经验丰富的神经放射科医师的认可。结论本文介绍的方法包括数据处理、模型训练和结果分析,重点是选择和开发高质量的计算机视觉算法,用于识别核磁共振成像图像中的多发性硬化病灶。脱髓鞘病灶的识别和分割是诊断疾病的必要步骤,也是计算和解释更有意义的疾病严重程度和进展指标的必要步骤。
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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