基于阶梯结构的U-Net结肠息肉分割算法

结肠息肉的精确分割对结直肠癌的诊断和治疗具有重要意义，目前的分割方法普遍存在有伪影、分割精度低等问题。该文提出一种基于阶梯结构的U-Net结肠息肉分割算法(SU-Net)，使用U-Net的U型结构，利用Kronecker乘积来扩展标准空洞卷积核，构成Kronecker空洞卷积下采样有效扩大感受野，弥补传统空洞卷积容易丢失的细节特征；应用具有阶梯结构的融合模块，遵循扩展和堆叠原则形成阶梯状的分层结构，有效捕获上下文信息并从多个尺度聚合特征；在解码器引入卷积重构上采样模块生成密集的像素级预测图，捕获双线性插值上采样中缺少的精细信息。在Kvasir-SEG数据集和CVC-EndoSceneStill数据集上对模型进行了测试，相似系数(Dice)指标和交并比(IoU)指标分别达到了87.51%, 88.75%和82.30%, 85.64%。实验结果表明，该文所提方法改善了因过度曝光、低对比度引起的分割精度低的问题，同时消除了边界外部的图像伪影和图像内部不连贯的现象，优于其他息肉分割方法。

Polyp Segmentation Using Stair-structured U-Net

The precise segmentation of colon polyps plays a significant role in the diagnosis and treatment of colorectal cancer. The existing segmentation methods have generally artifacts and low segmentation accuracy. In this paper, Stair-structured U-Net (SU-Net) is proposed to segment polyp, using U-shaped structure. The Kronecker product is used to extend the standard atrous convolution kernel to keep more detail structrural features that are easily ignored. Stair-structured fusion module is applied to encompass effectively multi-scale features. The decoder introduces a convolutional reshaped upsampling module to generate pixel-level predictions. Experiments are performed on the Kvasir-SEG dataset and the CVC-EndoSceneStill dataset. The results show that the method proposed in this paper outperforms other polyp segmentation methods in Dice and Intersection-over-Union(IoU).