Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement
Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement
复制标题
使用具有中智增强功能的显着性检测网络提取结直肠息肉区域
DOI：
10.1016/j.compbiomed.2022.105760
复制
发表时间：
2022-07-06
影响因子：
7.7
通讯作者：
Guo, Yanhui
中科院分区：
文献类型：
--
作者：
Hu, Keli;Zhao, Liping;Guo, Yanhui
大肠息肉的识别对于大肠癌的早期检测和治疗至关重要。结肠镜常用于结直肠息肉的检查。然而，由于息肉和正常组织的相似性，四分之一的息肉可能被忽略。在本文中，我们提出了一种新的方法，称为NeutSS-PLP的息肉区域提取结肠镜图像使用短连接的显着性检测网络与神经网络增强。我们首先利用镜面反射理论来提高结肠镜图像中镜面反射检测的质量。我们开发的局部和全局阈值标准在单值的自适应集（SVNS）域，并定义相应的T（真），I（不确定性），和F（虚假性）功能为每个标准。对构造良好的光学图像进行处理，并用于镜面反射检测和抑制。接下来，我们将两级短连接引入显著性检测网络，旨在利用从网络的不同阶段提取的多层次和多尺度特征。在两个公开的大肠息肉数据集上进行的实验结果分别达到了0.877和0.9135 mIoU的息肉提取，与几个最先进的显着性网络和语义分割网络相比，我们的方法表现得更好，这证明了显着性检测机制应用于大肠息肉区域提取的有效性。
Colorectal polyp recognition is crucial for early colorectal cancer detection and treatment. Colonoscopy is always employed for colorectal polyp scanning. However, one out of four polyps may be ignored, due to the similarity of polyp and normal tissue. In this paper, we present a novel method called NeutSS-PLP for polyp region extraction in colonoscopy images using a short connected saliency detection network with neutrosophic enhancement. We first utilize the neutrosophic theory to enhance the quality of specular reflections detection in the colonoscopy images. We develop the local and global threshold criteria in the single-valued neutrosophic set (SVNS) domain and define the corresponding T (Truth), I (Indeterminacy), and F (Falsity) functions for each criterion. The well-built neutrosophic images are processed and employed for specular reflection detection and suppressing. Next, we introduce two-level short connections into the saliency detection network, aiming to take advantage of the multi-level and multi-scale features extracted from different stages of the network. Experimental results conducted on two public colorectal polyp datasets achieve 0.877 and 0.9135 mIoU for polyp extraction respectively, and our method performs better compared with several state-of-the-art saliency networks and semantic segmentation networks, which demonstrate the effectiveness of applying the saliency detection mechanism for colorectal polyp region extraction.