Abstract
To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.
Highlights
With the development of medicine, more and more medical imaging images need to be processed, and image processing technology has become more and more important [1]
atrous spatial pyramid pooling (ASPP) was changed to 4 parallel 3 × 3 expansion convolution operations, rates (1, 6, 12, 18)
By comparing the experimental results, we can see that from the traditional segmentation technology in reference [14] to the deep learning algorithm in reference [21] and to the improved U-Net network of the proposed technology, the segmentation effect and robustness of the nucleus are getting better and better. e segmentation effect of edge details and the smaller nucleus is getting better and better. e experimental results show that the feature fusion method of skip connection and feature splicing in the U-Net network significantly improves the effect of U-Net image segmentation
Summary
With the development of medicine, more and more medical imaging images need to be processed, and image processing technology has become more and more important [1]. Reference [14] proposed an improved multi-level threshold image segmentation method based on differential evolution. Compared with traditional medical image segmentation methods, its innovations are (1) To solve the problems of poor segmentation of small nucleus, rough edges, and under- and oversegmentation, the U-Net network is used for image segmentation It stitches feature maps in the channel dimension to achieve feature fusion and uses a skip structure to combine low- and high-level features to ensure the segmentation effect of the nucleus. E classic image classification network with the fully connected layer removed is usually used It performs a convolution kernel pooling operation on the original input picture, which can obtain contextual semantic information to solve the classification problem in image segmentation. There is a convolutional layer of 1 × 1 to map the required number of feature vectors, and the convolution is an unpadding structure
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