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Real-Time 3D Scene Reconstruction for Surgery

A new method enhances 3D reconstruction from endoscopic videos for surgical applications.

― 6 min read


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Table of Contents

3D scene reconstruction from Endoscopic videos is important for improving surgical procedures. This process involves creating a 3D model of the surgical area using videos taken by a special camera designed for internal examinations. When we can accurately create these models in real-time, it helps surgeons understand the environment better and perform tasks more effectively.

This article discusses a new method for online 3D Reconstruction and Tracking, specifically designed for videos taken during endoscopic procedures. Our approach focuses on accurately modeling the surgical scene as it changes, which is crucial because tissues in the body can move and deform.

Importance of 3D Reconstruction in Surgery

Having a clear 3D representation of the surgical site can greatly benefit various tasks. For example, it can aid in surgical training, allow surgeons to see overlays of past images, and improve the functioning of robotic surgical systems. Therefore, having tools that can provide real-time and reliable 3D models of surgical areas is essential for the future of surgical assistance.

Recent advancements in technology have led to the development of promising methods for 3D reconstruction. Many of these methods use neural techniques but may face limitations, such as requiring a lot of processing time or not being able to handle tissue movements. Other methods have shown potential but require further adjustment to work effectively in surgical settings.

Our Approach

Our work centers on creating 3D models from endoscopic video data. We developed a system that can track dense points in a video while continuously updating the model as new parts of the scene become visible. Our technique uses a method called Gaussian splatting for quick model updates and includes a way to manage tissue movements by using a small number of key points.

We also designed a fitting algorithm that helps fine-tune the Model Parameters, ensuring that tracking and reconstruction remain accurate. Our experiments show that our method works effectively, surpassing other existing tracking algorithms and performing comparably to methods that process data offline.

Scene Representation

Our scene reconstruction system is built to track surface points across video frames. Each video frame consists of a color image, depth information, and the position of the camera. Our tracking method combines static scene elements with changes that occur in the tissue during surgery.

The model we created includes a rigid component, which is the fixed part of the scene, represented by a collection of colored points called Gaussians. Tissue movements are modeled by adding shifts and rotations to these points.

To manage tissue changes, we use Control Points, which help define how the scene is altered during surgery. These control points have positions, translations, and rotation changes that help represent the movements of the tissue accurately. We use a mathematical approach to blend the effects of these control points on the main points in the scene.

Rendering Images

To create a visual representation of the scene, our method takes the collection of points and processes them through a rendering function. This function generates the color of each pixel based on the positions and characteristics of the Gaussian points. We generate images that show both color and depth to provide a full view of the surgical area.

Online Model Fitting

Our model fitting process works continuously as new video frames are received. For each new frame, we adjust the model parameters to minimize discrepancies between what is observed in the video and what the model predicts. Our fitting process consists of several steps:

  1. Updating the Canonical Scene: As the camera moves, we cannot create a fixed scene at the beginning. Instead, we progressively add new points to cover newly visible areas.

  2. Control Point Setup: Control points are placed at specific locations based on the existing points. We use optical flow, a technique that calculates motion between frames, to set these points effectively.

  3. Minimizing Differences: Finally, we work to minimize the differences between the observed and predicted images, adjusting the model accordingly to improve fit and accuracy.

Experimental Results

To test our method, we used a publicly available dataset that includes various surgical scenes. This dataset was selected for its challenging situations, including tissue movements and occlusions. We evaluated our tracking method by manually annotating key points for comparison.

In our experiments, we compared our method to existing techniques to see how well it performed. Our results showed that our approach consistently outperformed others in different cases and achieved a perfect tracking success rate in some scenarios.

Our method proved robust in facing obstacles, such as occlusions caused by surgical instruments or by rapid camera movements. While traditional methods struggled in tracking during long occlusions, our system managed to maintain tracking effectively.

However, there were some areas where our method faced challenges. In cases with repetitive or little texture, long occlusions sometimes led to difficulties in accurately modeling tissue movements. This occurred because the system didn't observe the tissue changes during those periods, affecting the reconstruction.

Comparison with Offline Methods

We also compared our method with established offline reconstruction techniques. Despite being an online method, our approach showed performance levels similar to these traditional methods while being significantly faster. This indicates that online processing can meet the requirements for surgical assistance without sacrificing quality.

Additional Studies

As part of our research, we conducted a study to analyze the contributions of different components of our method. We tested variations of our approach to understand which elements were most impactful. The findings highlighted that several features, such as the energy equations and the use of control points, were instrumental in improving performance.

Application in 3D Segmentation

Our method not only provides 3D tracking but also supports further tasks, such as 3D semantic segmentation. This allows us to categorize different parts of the scene, like organs or surgical tools. By applying a segmentation network, we can assign labels to various elements, making it easier to analyze the scene comprehensively.

Conclusion

In summary, we developed a framework for online 3D scene reconstruction and tracking from stereo endoscopic videos. By representing the scene with Gaussian points and accounting for tissue deformations using control points, we achieved an effective real-time solution. Our method demonstrates significant potential for various applications, including surgical training and augmented reality systems. Future improvements should focus on increasing processing speed for real-time use and enhancing long-term tracking abilities in complex surgical scenes.

Original Source

Title: Online 3D reconstruction and dense tracking in endoscopic videos

Abstract: 3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene reconstruction and tracking, aimed at enhancing surgical scene understanding and assisting interventions. Our method dynamically extends a canonical scene representation using Gaussian splatting, while modeling tissue deformations through a sparse set of control points. We introduce an efficient online fitting algorithm that optimizes the scene parameters, enabling consistent tracking and accurate reconstruction. Through experiments on the StereoMIS dataset, we demonstrate the effectiveness of our approach, outperforming state-of-the-art tracking methods and achieving comparable performance to offline reconstruction techniques. Our work enables various downstream applications thus contributing to advancing the capabilities of surgical assistance systems.

Authors: Michel Hayoz, Christopher Hahne, Thomas Kurmann, Max Allan, Guido Beldi, Daniel Candinas, ablo Márquez-Neila, Raphael Sznitman

Last Update: Sep 9, 2024

Language: English

Source URL: https://arxiv.org/abs/2409.06037

Source PDF: https://arxiv.org/pdf/2409.06037

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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