基于改进U-Net的宫颈细胞核图像分割

原始的U-Net采用跳跃结构结合高低层的图像信息,使得U-Net模型有良好的分割效果,但是分割结果在宫颈细胞核边缘依然存在分割欠佳、过分割和欠分割等不足.由此提出了改进型U-Net网络图像分割方法.首先将稠密连接的DenseNet引入U-Net的编码器部分,以解决编码器部分相对简单,不能提取相对抽象的高层语义特征.然后对二元交叉熵损失函数中的宫颈细胞核和背景给予不同的权重,使网络更加注重细胞核特征的学习.最后在池化操作过程中,对池化域内的像素值分配合理的权值,解决池化层丢失信息的问题.实验证明,改进型U-Net网络使宫颈细胞核分割效果更好,模型也越鲁棒,过分割和欠分割比率也越少.显然,改进型U-Net是更有效的图像分割方法.

Cervical Nucleus Segmentation Based on Improved U-Net

The original U-Net integrates a jumping structure with high-level and low-level image information, which makes the U-Net model perform well in segmentation, but the results still present poor segmentation, over-segmentation, and under-segmentation at the edges of cervical nucleus. Then an improved U-Net network for image segmentation is proposed. First, the densely connected DenseNet is introduced into the encoder of U-Net to solve the problem that the encoder is too simple to extract abstract high-level semantic features. Then different weights are given to the cervical nucleus nuclei and background in the binary cross-entropy loss function, so that the network pays more attention to the learning of nuclear characteristics. Finally, during the pooling operation, reasonable weights are assigned to the pixel values in the pooling domain to avoid losing information in the pooling layer. Experimental results reveal that the improved U-Net network can behave better in cervical cell segmentation with a more robust model, and the proportions of over-segmentation and under-segmentation are also smaller.