Nadeem Saad, Kaufman Arie
Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA.
Proc SPIE Int Soc Opt Eng. 2016 Feb-Mar;9785. doi: 10.1117/12.2216996. Epub 2016 Mar 24.
We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, more than 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.
我们提出了一种用于光学结肠镜图像中息肉的计算机辅助检测算法。息肉是结肠癌的前身。仅在美国，每年就进行超过1400万次光学结肠镜检查，主要是为了筛查息肉。由于结肠中存在的复杂褶皱和弯曲，光学结肠镜检查已显示出约25%的息肉漏检率。在这项工作中，我们提出了一种自动检测算法，用于在光学结肠镜图像中检测这些息肉。我们使用机器学习算法为给定的光学结肠镜图像推断深度图，然后使用详细的预建息肉轮廓来检测和勾勒该给定图像中息肉的边界。我们实现了84.0%的最佳召回率和83.4%的最佳特异性值。
