Akbari Mojtaba, Mohrekesh Majid, Nasr-Esfahani Ebrahim, Soroushmehr S M Reza, Karimi Nader, Samavi Shadrokh, Najarian Kayvan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:69-72. doi: 10.1109/EMBC.2018.8512197.
Colorectal cancer is 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 the convolutional neural network. Two strategies enhance the performance of the method. First, we perform a novel image patch selection method in the training phase of the network. Second, in the test phase, we perform 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.
结直肠癌是癌症相关死亡的主要原因之一，在男性中尤为如此。息肉是结直肠癌的主要病因之一，通过结肠镜检查早期诊断息肉可实现成功治疗。由于息肉大小和形状的差异，在结肠镜检查视频中诊断息肉是一项具有挑战性的任务。在本文中，我们提出了一种基于卷积神经网络的息肉分割方法。两种策略提高了该方法的性能。首先，我们在网络训练阶段执行一种新颖的图像块选择方法。其次，在测试阶段，我们对网络生成的概率图进行有效的后处理。使用CVC-ColonDB数据库对所提出方法进行评估表明，与先前的结肠镜检查视频分割方法相比，我们提出的方法取得了更准确的结果。
