Abstract:

Aiming at the problems such as lack of contextual information connection， inaccurate and low-precision segmentation of cervical cell nucleus images， a cervical cell nucleus segmentation network named DGU-Net （Dense-Guided-UNet） was proposed on the basis of improved U-net combined with dense block and U-shaped convolutional multi-scale guided filtering module， which could segment cervical cell nucleus images more completely and accurately. Firstly， the U-net model with encoder and decoder structures was used as backbone of the network to extract image features. Secondly， the dense block module was introduced to connect the features between different layers， so as to realize transmission of contextual information， thereby enhancing feature extraction ability of the model. Meanwhile， the multi-scale guided filtering module was introduced after each downsampling and before each upsampling to introduce obvious edge detail information in the grayscale guided image for enhancement of the image details and edge information. Finally， a side output layer was added to each decoder path， so as to fuse and average all the output feature information， thereby fusing the feature information of different scales and levels to increase accuracy and completeness of the results. Experiments were conducted on Herlev dataset and the proposed network was compared with three deep learning models： U-net， Progressive Growing of U-net+ （PGU-net+）， and Lightweight Feature Attention Network （LFANet）. Results show that compared with PGU-net+， DGU-Net increases the accuracy by 70.06%； compared with LFANet， DGU-Net increases the Intersection-over-Union （IoU） by 6.75%. It can be seen that DGU-Net is more accurate in processing edge detail information， and outperforms the comparison models in segmentation indicators generally.

Key words: multi-scale guided filtering, dense block, cervical cell nucleus, cell nucleus image segmentation, U-shaped network

摘要：

针对宫颈细胞核图像分割中上下文信息联系匮乏和边缘分割不准确且精度低等问题，提出一种基于U-net改进的结合密集块的U型卷积多尺度引导滤波模块的宫颈细胞核分割网络DGU-Net （Dense-Guided-UNet），可以更完整且精确地分割宫颈细胞核图像。首先，以编码器、解码器结构的U-net模型作为网络骨干提取图像特征；其次，引入密集块模块连接不同层之间的特征，实现上下文信息的传递，从而增强模型的特征提取能力；同时，在每次下采样后和上采样前引入多尺度引导滤波模块，从而引入灰度引导图像中明显的边缘细节信息，增强图像细节和边缘信息；最后，在每个解码器路径中都增加一个侧输出层，融合并平均所有输出的特征信息，从而融合不同尺度不同层次的特征信息，提升结果的准确性和完整性。在Herlev数据集上实验，并把所提网络与U-net、PGU-net+ （Progressive Growing of U-net+）和LFANet （Lightweight Feature Attention Network）这3种深度学习模型对比。结果表明，与PGU-net+相比，DGU-Net的准确率提升了70.06%；与LFANet相比，DGU-Net的交并比（IoU）提升了6.75%。可见，DGU-Net在边缘细节信息处理上更准确，并在分割指标上普遍优于对比模型。

关键词: 多尺度引导滤波, 密集块, 宫颈细胞核, 细胞核图像分割, U型网络