Endoscopic Depth Estimation Based on Deep Learning: A Survey

TL;DR

This survey reviews recent deep learning techniques for endoscopic depth estimation, highlighting data, methods, and clinical applications, and discusses future research directions for medical imaging and minimally invasive surgery.

Contribution

It provides a comprehensive overview of recent deep learning-based endoscopic depth estimation methods, datasets, and clinical challenges, filling a gap left by previous surveys.

Findings

Summarizes publicly available datasets for endoscopic depth estimation.

Reviews monocular and stereo deep learning approaches.

Identifies challenges and solutions for clinical implementation.

Abstract

Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotics. Over the past decade, a large number of methods have been developed. Despite the existence of several related surveys, a comprehensive overview focusing on recent deep learning-based techniques is still limited. This paper endeavors to bridge this gap by systematically reviewing the state-of-the-art literature. Specifically, we provide a thorough survey of the field from three key perspectives: data, methods, and applications. Firstly, at the data level, we describe the acquisition process of publicly available datasets. Secondly, at the methodological level, we introduce both monocular and stereo deep learning-based approaches for endoscopic depth estimation. Thirdly, at the application level, we identify the specific challenges and corresponding solutions for the clinical implementation of depth estimation technology, situated within concrete clinical scenarios. Finally, we outline potential directions for future research, such as domain adaptation, real-time implementation, and the synergistic fusion of depth information with sensor technologies, thereby providing a valuable starting point for researchers to engage with and advance the field toward clinical translation.