论文标题
CLTS-GAN：结肠镜检查的颜色光质量纹理反射增强
CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy
论文作者
论文摘要
光学结肠镜检查（OC）视频帧的自动分析（在OC期间协助内窥镜）由于颜色，照明，纹理和镜面反射的变化而具有挑战性。先前的方法要么通过预处理（使管道繁琐）删除其中一些变化，要么用注释（但昂贵且耗时）添加多种培训数据。我们提出了CLTS-GAN，这是一种新的深度学习模型，可很好地控制OC视频帧的颜色，照明，纹理和镜面反射综合。我们表明，在培训数据中添加这些特定于结肠镜检查的增强功能可以改善最先进的息肉检测/分割方法，并驱动下一代OC模拟器用于培训医学生。 CLTS-GAN的代码和预训练模型可在计算内窥镜平台GitHub（https://github.com/nadeemlab/cep）上获得。
Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. The code and pre-trained models for CLTS-GAN are available on Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP).
