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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.
Tuchinov et al. (Thu,) studied this question.