Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information
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DOI：
10.1109/tmi.2015.2487997
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发表时间：
2016-02-01
影响因子：
10.6
通讯作者：
Liang, Jianming
中科院分区：
文献类型：
--
作者：
Tajbakhsh, Nima;Gurudu, Suryakanth R.;Liang, Jianming
本文介绍了我们在设计结肠镜视频中息肉的计算机辅助检测（CAD）系统方面的研究成果。我们的系统基于混合上下文-形状方法，利用上下文信息去除非息肉结构，利用形状信息可靠地定位息肉。具体来说，给定结肠镜图像，我们首先获得一个粗糙的边缘图。其次，我们使用我们独特的特征提取和边缘分类方案从边缘图中去除非息肉边缘。第三，我们使用新的投票方案在改进的边缘图中定位具有概率置信度分数的息肉候选者。建议的CAD系统已经使用两个公共息肉数据库进行了测试，CVC-ColonDB包含300个结肠镜图像，其中包含15个独特息肉的300个息肉实例，以及ASU-Mayo数据库，该数据库是我们的结肠镜视频集合，包含19,400帧和来自10个独特息肉的5,200个息肉实例。我们使用自由响应接收机工作特性（FROC）分析评估了我们的系统。在每帧0.1个误报的情况下，我们的系统对CVC-ColonDB的灵敏度为88.0%，对ASU-Mayo数据库的灵敏度为48%。此外，我们使用新的检测延迟分析来评估我们的系统，其中延迟被定义为从结肠镜检查视频中首次出现息肉到我们的系统首次检测到息肉的时间。在每帧0.05个误报的情况下，我们的系统产生的息肉检测延迟为0.3秒。
This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.