Accurate and fast monocular endoscopic depth estimation of structure-content integrated diffusion

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2025-09-06 DOI:10.1016/j.compmedimag.2025.102640

Min Tan , Yushun Tao , Boyun Zheng , Gaosheng Xie , Zeyang Xia , Jing Xiong

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引用次数: 0

Abstract

Endoscopic depth estimation is crucial for video understanding, robotic navigation, and 3D reconstruction in minimally invasive surgeries. However, existing methods for monocular depth estimation often struggle with the challenging conditions of endoscopic imagery, such as complex illumination, narrow luminal spaces, and low-contrast surfaces, resulting in inaccurate depth predictions. To address these challenges, we propose the Structure-Content Integrated Diffusion Estimation (SCIDE) for accurate and fast endoscopic depth estimation. Specifically, we introduce the Structure Content Extractor (SC-Extractor), a module specifically designed to extract structure and content priors to guide the depth estimation process in endoscopic environments. Additionally, we propose the Fast Optimized Diffusion Sampler (FODS) to meet the real-time needs in endoscopic surgery scenarios. FODS is a general sampling mechanism that optimizes selection of time steps in diffusion models. Our method (SCIDE) shows remarkable performance with an RMSE value of 0.0875 and a reduction of 74.2% in inference time when using FODS. These results demonstrate that our SCIDE framework achieves state-of-the-art accuracy of endoscopic depth estimation, and making real-time application feasible in endoscopic surgeries. https://misrobotx.github.io/scide/

查看原文本刊更多论文

结构-含量集成扩散的准确快速单眼内窥镜深度估计

内镜深度估计对于微创手术中的视频理解、机器人导航和3D重建至关重要。然而，现有的单目深度估计方法经常与内窥镜图像的挑战性条件作斗争，例如复杂的照明，狭窄的腔空间和低对比度的表面，导致深度预测不准确。为了解决这些挑战，我们提出了结构-内容集成扩散估计（SCIDE），用于准确快速的内窥镜深度估计。具体来说，我们介绍了结构内容提取器（SC-Extractor），这是一个专门用于提取结构和内容先验的模块，以指导内镜环境下的深度估计过程。此外，我们提出了快速优化扩散采样器（FODS），以满足内镜手术场景的实时需求。FODS是一种通用的采样机制，可以优化扩散模型中时间步长的选择。我们的方法（SCIDE）在使用FODS时表现出了显著的性能，RMSE值为0.0875，推理时间减少了74.2%。这些结果表明，我们的SCIDE框架达到了最先进的内窥镜深度估计精度，并使实时应用于内窥镜手术成为可能。https://misrobotx.github.io/scide/

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来源期刊

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.