A Practical Polyp Detecting Model in Colonoscopy Video by Post-processing

Zhipeng Zhang, Ling Ma, Y-D. Chana, Li Xiao, Qing He, Conghui Ma, Huiqin Jiang
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Abstract

Polyp recognition in colonoscopy video is crucial for early colorectal cancer detection and treatment. However, polyps are very similar to other intestinal tissues. Intestinal peristalsis and debris shelter lead to great changes in polyp morphology. Lens motion may lead to blurred images. Traditional target detection methods can not meet the needs of the complex environment. In previous work, we propose top likelihood loss and similarity loss to solve the false positive problem. However, when detecting video polyps, there are great problems in the previous work due to the more complex video environment. In this work, we develop the new video detection mode based on our previous work. We add a new post-processing method in the prediction part of YOLOv4 model, which uses the front and rear neighborhood frames to judge the accuracy of current frame detection, combine the single frame detection results and spatiotemporal information, and then make the final decision. We test our method on two datasets, One is the private dataset we collect, and the other is the public dataset. Compared with the baseline, our method has a great improvement in accuracy. Ours method is superior to the most advanced target detection methods that can meet real-time constraints.
基于后处理的实用结肠镜视频息肉检测模型
结肠镜影像中息肉的识别对结直肠癌的早期发现和治疗至关重要。然而,息肉与其他肠道组织非常相似。肠道蠕动和碎片遮蔽导致息肉形态发生巨大变化。镜头运动可能导致图像模糊。传统的目标检测方法已不能满足复杂环境的需要。在以前的工作中,我们提出了最高似然损失和相似损失来解决假阳性问题。然而,由于视频环境较为复杂,在检测视频息肉时,以往的工作存在很大的问题。在本工作中,我们在前人工作的基础上开发了新的视频检测模式。我们在YOLOv4模型的预测部分增加了一种新的后处理方法,利用前后邻域帧来判断当前帧检测的准确性,将单帧检测结果与时空信息相结合,再做出最终决策。我们在两个数据集上测试我们的方法,一个是我们收集的私有数据集,另一个是公共数据集。与基线相比,我们的方法在精度上有很大的提高。我们的方法优于最先进的目标检测方法,可以满足实时约束。
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