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A New Multilevel Feature Fusion Network for Medical Image Segmentation

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Abstract

There are many problems in the medical images, such as irregular edges and noises, the pathological information is difficult to be obtained. So it brings difficulties to doctors to accurately diagnose the diseases. Therefore, in medical image processing, traditional image segmentation methods are based on region similarity and region difference. Medical image segmentation plays a very important role in the field of clinical medicine research. Meanwhile, deep learning methods used for medical image segmentation can abstract image segmentation as a problem of feature representation and parameter optimization. In order to solve the problem of losing feature information in the process of up-sampling and down-sampling, a new multilevel feature fusion network is proposed for medical image segmentation. Experiments on the open data set show that the proposed method can effectively improve the segmentation accuracy.

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  1. Zhengzhou University of Industrial Technology, Zhengzhou, 450000, China

    Xiaojing Qiu

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  1. Xiaojing Qiu
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Correspondence to Xiaojing Qiu.

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Qiu, X. A New Multilevel Feature Fusion Network for Medical Image Segmentation. Sens Imaging 22, 23 (2021). https://doi.org/10.1007/s11220-021-00346-2

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  • DOI: https://doi.org/10.1007/s11220-021-00346-2

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