论文标题
自动生成域随机化的合成结肠镜检查视频
Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization
论文作者
论文摘要
越来越多的结肠镜指导和援助系统依赖于需要大量高质量培训数据的机器学习算法。为了确保高性能，后者必须类似于大部分可能的配置。这尤其解决了不同的解剖结构，粘膜外观和图像传感器特征，这些特性可能会因运动模糊而恶化和照明不足而恶化。有限的可用培训训练器数量有限，以说明所有这些可能的配置，从而降低了机器学习模型的概括能力。我们提出了一种示例性的解决方案，用于合成具有实质性外观和解剖学变化的结肠镜检查视频，该视频使内部结肠的判别域随机表示，同时模仿现实世界的设置。
An increasing number of colonoscopic guidance and assistance systems rely on machine learning algorithms which require a large amount of high-quality training data. In order to ensure high performance, the latter has to resemble a substantial portion of possible configurations. This particularly addresses varying anatomy, mucosa appearance and image sensor characteristics which are likely deteriorated by motion blur and inadequate illumination. The limited amount of readily available training data hampers to account for all of these possible configurations which results in reduced generalization capabilities of machine learning models. We propose an exemplary solution for synthesizing colonoscopy videos with substantial appearance and anatomical variations which enables to learn discriminative domain-randomized representations of the interior colon while mimicking real-world settings.
