EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth Prediction

TL;DR

This paper introduces EndoDepth, a comprehensive benchmark with a new dataset and evaluation metrics to assess and improve the robustness of monocular depth prediction models specifically in endoscopic medical imaging scenarios.

Contribution

The paper presents the EndoDepth benchmark, including a novel dataset and a composite robustness metric, to evaluate and enhance depth prediction models in challenging endoscopic environments.

Findings

State-of-the-art models show varied robustness to endoscopic artifacts.

The EndoDepth benchmark reveals specific strengths and weaknesses of current models.

Specialized techniques are necessary for accurate depth estimation in endoscopy.

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.