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• W4297341772 abstract "Abstract Due to the unresectability of normal brain tissue and the extensive invasive growth of the malignant tumor, the boundary between the tumor and surrounding healthy brain tissue or blood vessels is blurred, which greatly affects the accuracy of diagnosis and treatment. Meanwhile, with the growth of data information and the development of computer equipment, it is extremely time-consuming and laborious to rely on the traditional manual segmentation of brain medical images. To solve the above problems, this paper proposes a multi-input Unet model based on the integrated block and the aggregation connection to achieve efficient and accurate segmentation of tumor structure. Besides, this paper studies two-dimensional (2D) transverse brain tumor slices to meet the needs of doctors in the actual diagnosis. It solves the low-resolution problem in sagittal and coronal planes, which can effectively improve memory efficiency. The proposed algorithm is innovative in three aspects. Firstly, by inputting the mask images which can effectively represent the tumor location characteristics, it can provide more information about the spatial relationship to alleviate the problems of fuzzy boundary and low contrast between the lesion region and healthy brain tissue. Then, the integrated block extracts the tumor local information in different receptive domains by a multi-scale convolution kernel. The aggregation connection realizes the implicit deep connection of context information, which combines the shallow and deep information of the brain with strong geometric spatial relationships. Meanwhile, to effectively alleviate the waste of memory resources caused by redundant and background information in medical images, the amount of calculation in model training is reduced by dimension reduction of the feature map. It can also overcome the gradient vanishing problem caused by network deepening. In this paper, an ablation experiment is used to verify the innovation of the proposed algorithm on the BraTS dataset, which compares with the state-of-the-art brain tumor segmentation methods. The accuracy of the proposed multi-input Unet model for the whole tumor and core lesion is 0.92 and 0.90, respectively." @default.
• W4297341772 created "2022-09-28" @default.
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• W4297341772 date "2022-09-27" @default.
• W4297341772 modified "2023-10-17" @default.
• W4297341772 title "Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation" @default.
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• W4297341772 doi "https://doi.org/10.21203/rs.3.rs-1014002/v1" @default.
• W4297341772 hasPublicationYear "2022" @default.
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