SCARED-C

The dataset SCARED-C is introduced in the context of assessing robustness in endoscopic depth prediction models. It is part of the EndoDepth benchmark, which is designed to evaluate the performance of monocular depth prediction models specifically for endoscopic scenarios. The dataset features 16 different types of image corruptions, each with five levels of severity, encompassing challenges like lens distortion, resolution alterations, specular reflection, and color changes that are typical in endoscopic imaging. The ground truth is on the original testing set of SCARED.
The purpose of SCARED-C is to test the robustness of depth estimation models by exposing them to various common endoscopic corruptions. This dataset is a valuable tool for developing and evaluating depth prediction algorithms that can handle the unique challenges presented by endoscopic procedures, ensuring more accurate and reliable outcomes in medical imaging.

数据集SCARED-C是为评估内窥镜深度预测模型的鲁棒性而提出的。该数据集隶属于EndoDepth基准测试，后者专为内窥镜场景下单目深度预测模型的性能评估而设计。此数据集包含16种不同类型的图像损坏，每种损坏均设有5个严重程度等级，覆盖了内窥镜成像中常见的镜头畸变、分辨率变更、镜面反射与色彩变化等各类典型挑战。其真值标签源自SCARED的原始测试集。
SCARED-C的设计初衷是通过向深度估计模型施加各类常见的内窥镜图像损坏，以此测试模型的鲁棒性。该数据集是开发与评估能够应对内窥镜诊疗流程独特挑战的深度预测算法的宝贵工具，有助于保障医学影像中深度预测结果的准确性与可靠性。