Segmentation Method of Knee Meniscus Based on Multiscale-net

Abstract

Abstract: The accuracy of knee meniscus segmentation was of great significance to the discrimination and diagnosis of meniscus tear grade. In order to improve the segmentation accuracy， this paper proposed a knee meniscal segmentation method based on Multiscale-Net network. This method combined the convolution layer and pooling layer of visual geometry group network16 and the decoder part of U-Net， and it replaced the 3×3 convolution layer connected with the encoder and decoder with an improved atrous spatial pyramid pooling module. Finally， it was verified on the real data set of clinical patients provided by the first affiliated hospital of Anhui medical university and compared with U-Net， U-Net with ASPP module introduced， and other models. The experimental results showed that the intersection over union and dice similarity coefficient of this method reached 91.25% and 94.89% respectively.

Key words: meniscus image segmentation, convolutional neural network, U-Net network, spatial convolution pool pyramid, VGG16

WANG Juan, LI Chuan-geng, ZHANG Qing-yuan, XIA Cheng-yi. Segmentation Method of Knee Meniscus Based on Multiscale-net[J]. Computer and Modernization, 2023, 0(05): 111-116.

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