Leveraging Near-Field Lighting for Monocular Depth Estimation in Endoscopy Videos

Concepts de base

Utilizing near-field lighting for improved monocular depth estimation in endoscopy videos.

Résumé

The content discusses the challenges in depth estimation in endoscopy videos due to illumination effects and lack of geometric features. It introduces a novel approach using per-pixel shading representation and a depth refinement network (PPSNet) to enhance depth estimation. The teacher-student transfer learning method is proposed to improve depth maps from synthetic and clinical data. State-of-the-art results are achieved on the C3VD dataset, showcasing the effectiveness of the approach.

Stats

"In 2022 an estimated 225.3 million endoscopic procedures were performed globally with an annual growth rate of 1.3%."
"Our method outperforms the state-of-the-art monocular depth estimation technique LightDepth [54]."
"Our method is trained with only ∼18,000 clinical endoscopy images using lighting-based self-supervision."

Citations

"Our key idea is to model the reflection of co-located light with the camera of the endoscope from the surface of the internal organ to help predict the relative distance between the endoscope and the surface."
"We show that our proposed approach significantly outperforms state-of-the-art monocular depth estimation techniques on the synthetic C3VD dataset."
"Our method produces higher-quality depth maps with fewer discrepancies in the case of C3VD and clinical data."

Idées clés tirées de

Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos

by Akshay Paruc... à arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17915.pdf

Questions plus approfondies

How can the proposed approach be adapted for other medical imaging applications

The proposed approach can be adapted for other medical imaging applications by leveraging the concept of per-pixel shading representation and near-field lighting for depth estimation. For instance, in bronchoscopy, where a similar endoscopic imaging technique is used to examine the airways, the method can be applied to estimate the depth of structures within the airways. By utilizing the photometric cues from the light emitted by the bronchoscope and reflected by the airway surfaces, the algorithm can enhance the 3D understanding of the airway geometry. This can aid in better visualization, navigation, and detection of abnormalities in the airways, similar to its application in colonoscopy.

What are the potential limitations of relying on near-field lighting for depth estimation

One potential limitation of relying on near-field lighting for depth estimation is the sensitivity to variations in lighting conditions. Near-field lighting may not always provide consistent and reliable depth information, especially in scenarios where there are changes in the intensity or direction of the light source. This can lead to inaccuracies in depth estimation, particularly in areas with complex lighting effects or specular reflections. Additionally, the assumption of a co-located light source and camera may not always hold true in practical medical imaging setups, which can further impact the accuracy of depth maps.

How might the use of per-pixel shading representation impact the accuracy of depth maps in different lighting conditions

The use of per-pixel shading representation can impact the accuracy of depth maps in different lighting conditions by providing additional information about the interaction between light and surfaces. In scenarios with uniform lighting, the per-pixel shading representation can help capture subtle variations in surface geometry based on the intensity and direction of light. However, in challenging lighting conditions such as strong shadows, specular reflections, or uneven illumination, the per-pixel shading representation may introduce noise or artifacts in the depth estimation process. It is essential to carefully consider the impact of varying lighting conditions on the per-pixel shading representation to ensure robust and accurate depth estimation across different scenarios.