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摘要翻译：
结直肠癌是癌症相关死亡的最高原因之一，尤其是在男性中。息肉是结直肠癌的主要原因之一，通过结肠镜早期诊断息肉可成功治疗。由于息肉的大小和形状不同，在结肠镜视频中诊断息肉是一项具有挑战性的任务。本文提出了一种基于卷积神经网络的息肉分割方法。通过两种策略提高了该方法的性能。首先，我们在网络的训练阶段执行了一种新的图像贴片选择方法。其次，在测试阶段，对网络生成的概率图进行有效的后处理。利用CVC-ColonDB数据库对所提方法进行的评估表明，与以往的结肠镜视频分割方法相比，我们提出的方法获得了更准确的结果。
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英文标题：
《Polyp Segmentation in Colonoscopy Images Using Fully Convolutional
Network》
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作者：
Mojtaba Akbari, Majid Mohrekesh, Ebrahim Nasr-Esfahani, S.M. Reza
Soroushmehr, Nader Karimi, Shadrokh Samavi, Kayvan Najarian
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Colorectal cancer is a one of the highest causes of cancer-related death, especially in men. Polyps are one of the main causes of colorectal cancer and early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper we proposed a polyp segmentation method based on convolutional neural network. Performance of the method is enhanced by two strategies. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform an effective post processing on the probability map that is produced by the network. Evaluation of the proposed method using the CVC-ColonDB database shows that our proposed method achieves more accurate results in comparison with previous colonoscopy video-segmentation methods.
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PDF链接：
https://arxiv.org/pdf/1802.00368
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