Report on Current Developments in Endoscopic Depth Estimation and Polyp Detection

General Direction of the Field

The recent advancements in the field of endoscopic depth estimation and polyp detection are significantly pushing the boundaries of what is possible in medical imaging and computer-aided diagnosis. The focus is shifting towards developing more robust, accurate, and efficient models that can handle the complexities and variability inherent in endoscopic data. This shift is driven by the need for more reliable tools to assist clinicians in various medical procedures, particularly in the context of colonoscopy and ulcerative colitis assessment.

One of the key trends is the integration of spatio-temporal information into models, which allows for a more comprehensive understanding of the endoscopic environment. This is evident in the development of transformer-based architectures that can capture both spatial and temporal dependencies, leading to more accurate and consistent predictions. Additionally, there is a growing emphasis on creating specialized benchmarks and datasets that more accurately reflect the challenges encountered in real-world endoscopic procedures, such as image corruptions and intra-sequence heterogeneity.

Another notable trend is the move towards full-parameter and parameter-efficient learning frameworks, which aim to improve the adaptability and performance of pre-trained models when applied to endoscopic depth estimation. These frameworks are designed to optimize the model's ability to generalize across different datasets and conditions, thereby enhancing the robustness and reliability of the predictions.

Noteworthy Papers

• EndoDepth: Introduces a novel benchmark and composite metric for assessing depth estimation robustness in endoscopy, providing valuable insights for future research.
• TSdetector: Proposes a temporal-spatial self-correction detector that significantly improves polyp detection rates in colonoscopy videos, outperforming state-of-the-art methods.
• Arges: Utilizes a spatio-temporal transformer for accurate ulcerative colitis severity assessment, demonstrating significant improvements in disease scoring accuracy.
• Polyp-SES: Presents an automatic polyp segmentation method with self-enriched semantic features, achieving superior performance across multiple benchmarks.
• Automatic Image Unfolding and Stitching Framework: Introduces a novel framework for esophageal lining video stitching, enhancing the quality and continuity of endoscopic visual data.