提出新评估标准，测试内镜深度估计模型在图像失真下的可靠性。
Benchmarking Robustness of Endoscopic Depth Estimation with Synthetically Corrupted Data
- 用合成失真数据构建真实场景模拟的基准数据集。
- 引入综合误差、精度与鲁棒性的新评分指标DERS。
- 适合医疗AI研发者和内镜手术系统开发者参考。
准确的深度感知对内镜手术患者结局至关重要，但手术环境中的图像失真会严重影响其表现。为此，本研究构建了一个全面的基准数据集，模拟真实手术场景中多种合成失真，涵盖不同严重程度。我们提出深度估计鲁棒性评分（DERS），整合误差、准确性与鲁棒性，满足外科应用的多重需求。该指标为性能评估提供基础，建立深度估计技术对比分析的新范式。通过框架对两种单目深度估计模型的分析揭示了其在恶劣条件下的可靠性差异。结果强调算法需具备抗数据失真能力，推动模型鲁棒性提升。本研究不仅拓展理论框架，更直接促进手术精度与患者安全。代码已开源：https://github.com/lofrienger/EndoDepthBenchmark。
原文摘要 · Abstract (English)
Accurate depth perception is crucial for patient outcomes in endoscopic surgery, yet it is compromised by image distortions common in surgical settings. To tackle this issue, our study presents a benchmark for assessing the robustness of endoscopic depth estimation models. We have compiled a comprehensive dataset that reflects real-world conditions, incorporating a range of synthetically induced corruptions at varying severity levels. To further this effort, we introduce the Depth Estimation Robustness Score (DERS), a novel metric that combines measures of error, accuracy, and robustness to meet the multifaceted requirements of surgical applications. This metric acts as a foundational element for evaluating performance, establishing a new paradigm for the comparative analysis of depth estimation technologies. Additionally, we set forth a benchmark focused on robustness for the evaluation of depth estimation in endoscopic surgery, with the aim of driving progress in model refinement. A thorough analysis of two monocular depth estimation models using our framework reveals crucial information about their reliability under adverse conditions. Our results emphasize the essential need for algorithms that can tolerate data corruption, thereby advancing discussions on improving model robustness. The impact of this research transcends theoretical frameworks, providing concrete gains in surgical precision and patient safety. This study establishes a benchmark for the robustness of depth estimation and serves as a foundation for developing more resilient surgical support technologies. Code is available at https://github.com/lofrienger/EndoDepthBenchmark.
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