Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation. (January 2023) - Fang, Lingling; Wang, Xin
Abstract: 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 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. 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 precision of the proposed multi-input Unet model for the whole tumor and core lesion is 0.92 and 0.90, respectively.