EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction
EndoDepth benchmark evaluates robustness of monocular endoscopic depth prediction using mDERS and SCARED-C dataset, revealing strengths and weaknesses of SOTA models.
Key Findings
Methodology
This paper introduces the EndoDepth benchmark, integrating 16 types of endoscopy-specific corruptions at multiple severity levels. The core metric, mDERS, combines multiple error and accuracy measures to assess model robustness systematically. Four state-of-the-art models, including MonoViT and EndoSfMLearner, are evaluated on the SCARED-C dataset, which simulates realistic corruptions. The evaluation quantifies errors across different corruption types and severity levels, ensuring comprehensive and fair model assessment. The approach emphasizes robustness in challenging clinical scenarios, guiding future model development.
Key Results
- AF-SfMLearner achieved the highest mDERS score of 0.3134, indicating superior robustness under diverse corruptions. MonoDepth2, as a baseline, showed significantly higher errors, validating the effectiveness of the proposed evaluation. Corruption types like specular reflection and color shifts notably impacted performance, with models maintaining stability under moderate corruptions but degrading under severe conditions. The results demonstrate that model architecture and training data diversity critically influence robustness, with AF-SfMLearner balancing accuracy and error resistance effectively.
- Comparative analysis revealed MonoViT excelled in handling illumination variations, while EndoSfMLearner struggled with high severity corruptions. The new mDERS metric provided a more holistic robustness measure than traditional error metrics, highlighting the importance of multi-faceted evaluation. Experiments confirmed that robustness drops significantly with corruption severity, underscoring the need for models trained on diverse, corrupted data to ensure clinical reliability.
- The diverse corruption simulations and severity levels offered realistic challenges, pushing models toward greater resilience. Future work aims to enhance generalization, incorporate multi-modal data, and optimize for real-time deployment, ultimately improving endoscopic depth perception in clinical settings.
Significance
This work addresses a critical gap in evaluating the robustness of endoscopic depth prediction models, which is essential for safe and reliable clinical application. By systematically simulating real-world corruptions and introducing the mDERS metric, the study provides a standardized framework for assessing model resilience. The findings guide the development of more robust algorithms capable of handling the complex, noisy environments encountered during minimally invasive surgeries. Long-term, this research paves the way for autonomous, precise endoscopic navigation, reducing operator dependency and improving patient outcomes.
Technical Contribution
The primary technical innovation lies in the design of a comprehensive corruption simulation framework tailored for endoscopy, combined with the formulation of the mDERS metric that integrates multiple error and accuracy indicators. The framework covers 16 corruption types, including illumination, sensor failure, and image deformation, at five severity levels. The mDERS metric balances error and accuracy, providing a single, interpretable robustness score. This approach surpasses traditional metrics like mCE by avoiding baseline bias and offering a more nuanced evaluation, facilitating targeted model improvements.
Novelty
This is the first systematic robustness benchmark specifically designed for endoscopic depth prediction, incorporating realistic corruption simulations and a composite robustness score. Unlike prior general-purpose benchmarks, it captures the unique challenges of endoscopic imaging, such as specular reflections and rapid illumination changes. The introduction of SCARED-C dataset with 16 corruption types at multiple severity levels further distinguishes this work, establishing a new standard for evaluating and improving model resilience in clinical environments.
Limitations
- Despite extensive corruption simulation, real clinical scenarios may involve unforeseen artifacts not covered, limiting generalization. Models still degrade under extreme corruptions, indicating room for robustness enhancement. Computational costs for evaluating multiple corruption types and severity levels are high, restricting real-time deployment. Additionally, dataset size remains limited, and cross-device variability needs further validation. Future efforts should focus on lightweight models, broader corruption coverage, and multi-modal data integration to address these issues.
Future Work
Future research will explore multi-modal fusion, including ultrasound and optical coherence tomography, to improve robustness. Developing self-supervised and transfer learning strategies can reduce reliance on annotated data. Efforts will also focus on model lightweighting for real-time clinical use, expanding corruption types, and creating larger, more diverse datasets. Additionally, cross-domain validation and adaptation techniques will be investigated to enhance generalization across different endoscopic systems and patient populations.
AI Executive Summary
Endoscopic procedures are vital for minimally invasive diagnostics, yet accurate depth estimation remains challenging due to complex imaging artifacts. Existing models often falter under conditions like specular reflections, illumination variations, and sensor malfunctions. To address this, the study introduces the EndoDepth benchmark, a comprehensive evaluation framework that simulates 16 types of realistic corruptions at multiple severity levels, reflecting the diverse challenges faced during clinical endoscopy.
Central to this framework is the mean Depth Estimation Robustness Score (mDERS), a novel composite metric that combines multiple error and accuracy measures to provide a holistic assessment of model resilience. The evaluation includes four advanced models, notably AF-SfMLearner, which demonstrated the best robustness score of 0.3134, indicating a balanced trade-off between accuracy and error resistance. The experiments reveal that corruption types such as specular reflection and color shifts significantly impact performance, emphasizing the need for robustness-focused training.
The introduction of the SCARED-C dataset, with its extensive corruption scenarios, marks a significant step toward standardizing robustness evaluation in endoscopic depth prediction. This work not only highlights the current limitations of state-of-the-art models but also provides a pathway for future improvements, including multi-modal data fusion, lightweight architectures, and real-time deployment strategies. Ultimately, this research aims to enhance the safety, reliability, and automation of endoscopic surgeries, contributing to the broader goal of advancing minimally invasive medicine.
Deep Analysis
Background
随着深度学习在医学影像中的广泛应用，内窥镜作为微创诊断的重要工具，其深度估计技术逐渐成为研究焦点。早期多视角和结构光方法受限于设备复杂性和计算成本，难以实现实时应用。近年来，Eigen等提出的单目深度预测模型通过深度卷积网络取得突破，但在低纹理、强光照变化和镜面反射环境中表现不佳。自监督方法如SfMLearner系列减少了对标注数据的依赖，但在实际临床复杂场景中鲁棒性不足。现有数据集如EndoSfMLearner和SCARED提供基础，但缺乏系统性评估模型抗干扰能力的标准框架。为推动医学深度估计的临床应用，亟需建立专门针对腐蚀干扰的评估体系。
Core Problem
内窥镜图像常受光照不均、镜面反射、噪声和设备故障等影响，导致深度估计误差剧增。现有模型多在理想条件下训练，面对复杂环境性能大幅下降。缺乏针对这些特定腐蚀的鲁棒性评估指标，限制了模型的临床推广。如何在保证高精度的同时提升模型在多变环境中的稳定性，成为核心难题。现有评估体系多依赖单一指标，难以全面反映模型抗干扰能力。
Innovation
本研究创新点包括：1）设计结合多类型腐蚀模拟的多级严重度评估体系，真实还原临床环境干扰；2）提出mDERS指标，结合误差和准确性，系统衡量模型鲁棒性，避免偏向单一指标；3）构建SCARED-C数据集，涵盖16类腐蚀类型，丰富训练与测试场景。这些创新为内窥镜深度估计模型的鲁棒性提供了科学、全面的评估工具，推动模型在实际临床中的可靠性。
Plain Language Accessible to non-experts
想象你在厨房做饭，灯光忽明忽暗，锅里的汤有时会溅出，反光的锅盖也会反光。即使如此，你还是要确保菜做得好、味道正。深度估计模型就像厨师，要在各种干扰下依然能准确判断锅的深浅。研究中模拟了不同的干扰，比如光线变暗、反光、汤溅出，帮助模型变得更聪明、更稳健，能在真实厨房环境中表现更好。最终目标是让机器人医生在手术中也能像厨师一样，精准判断器官深度，即使环境复杂多变。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的游戏，你的任务是用一只手控制角色穿越迷宫。但突然，屏幕变得模糊、颜色变怪、出现闪光或噪点。这些干扰让你很难判断前方的路。这个研究就像是在测试不同的游戏角色（模型），让他们在各种干扰中依然能找到正确的路。科学家设计了很多模拟干扰的方法，比如变暗、反光、噪点，然后看看哪个角色（模型）还能表现得最好。最后发现某些角色（模型）在这些干扰中表现更稳，能帮医生在手术中更准确判断器官深度，就像游戏变得更公平、更有挑战性，帮助我们找到最厉害的玩家（模型）。
Abstract
Accurate depth estimation in endoscopy is vital for successfully implementing computer vision pipelines for various medical procedures and CAD tools. In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the robustness of monocular depth prediction models in endoscopic scenarios. Unlike traditional datasets, the EndoDepth benchmark incorporates common challenges encountered during endoscopic procedures. We present an evaluation approach that is consistent and specifically designed to evaluate the robustness performance of the model in endoscopic scenarios. Among these is a novel composite metric called the mean Depth Estimation Robustness Score (mDERS), which offers an in-depth evaluation of a model's accuracy against errors brought on by endoscopic image corruptions. Moreover, we present SCARED-C, a new dataset designed specifically to assess endoscopy robustness. Through extensive experimentation, we evaluate state-of-the-art depth prediction architectures on the EndoDepth benchmark, revealing their strengths and weaknesses in handling endoscopic challenging imaging artifacts. Our results demonstrate the importance of specialized techniques for accurate depth estimation in endoscopy and provide valuable insights for future research directions.