Deep learning for endoscopic depth estimation: A review

Depth estimation is a fundamental task in computer vision, crucial for applications such as endoscopic surgical navigation. This paper comprehensively reviews recent advancements in endoscopic depth estimation algorithms utilizing deep learning. We start by briefly describing the basic principles behind depth estimation and how depth maps can be generated from monocular and binocular cues. We then analyze the characteristics of the endoscopic dataset. Subsequently, we provide an overview of deep learning applications in endoscopic depth estimation, encompassing supervised, self-supervised, and semi-supervised learning methods. We examine each method’s principles, advantages, and disadvantages and their performance in practical applications. Additionally, we summarize the performance of current deep learning methods in endoscopic depth estimation and explore the importance of model robustness and generalization capabilities. Finally, we propose potential future research directions, such as exploring methods for collecting high-quality data or using simulated data to overcome current dataset limitations, and developing lightweight models to enhance real-time performance and robustness. This study aims to offer a comprehensive review for researchers in the field of endoscopic depth estimation, thereby fostering further development in this area. • Reviews the application of deep learning in endoscopic depth estimation. • Analyzes the characteristics of endoscopic datasets and metric selection. • Compares the advantages and disadvantages of supervised, self-supervised, and semi-supervised learning. • Proposes research directions for high-quality data collection and lightweight model development.