Surgical Light Camera Obstruction Removal
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Solution Overview
Problem
Surgical lights with embedded cameras capture images that are obstructed by the surgeon's head or body, limiting their usefulness during surgical procedures due to the cameras' placement for optimal illumination.
Innovation Solution
A method and system that receive and process images from multiple cameras to identify and exclude obstructions, generating a composite image that centrally positions the surgical site, using machine-learning models and sensors to determine obstructions and adjust camera orientations for unobstructed views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If surgical lights are placed close to and above the surgeon's head for optimal illumination, then illumination intensity is improved, but the surgeon's head or body obstructs the camera's field of view
Solution Approach 1:
The surgical lighting system is divided into multiple independent light units, each with its own camera. This segmentation allows the system to capture images from multiple perspectives simultaneously, enabling the reconstruction of unobstructed views even when individual cameras are blocked by the surgeon's body.
Solution Approach 2:
A computing device acts as an intermediary that receives images from multiple cameras, identifies obstructions using machine learning models, and generates composite images that eliminate obstructed areas. This intermediary processing transforms obstructed individual views into clear composite views of the surgical site.
2Device complexity
If a single camera is used in the surgical light, then device complexity is reduced, but the ability to capture unobstructed views is limited
Solution Approach 1:
Multiple images from different cameras are merged into a single composite image that combines the unobstructed portions from each view. This merging process creates a reliable, complete view of the surgical site that overcomes the limitations of individual obstructed cameras.
Solution Approach 2:
The system transitions from a single two-dimensional camera view to a multi-dimensional approach by capturing images from multiple spatial positions and angles. This dimensional expansion allows the system to overcome obstructions by selecting and combining views from different spatial dimensions.
3Reliability
If multiple cameras are used to capture images from different perspectives, then the ability to generate unobstructed composite images is improved, but device complexity increases
Solution Approach 1:
The system uses machine learning models that automatically identify obstructions and select appropriate image regions without requiring manual intervention. This self-service capability simplifies the operation of the complex multi-camera system, allowing it to autonomously generate reliable composite images despite the increased device complexity.
4Productivity
If images are processed in real-time to remove obstructions, then productivity is improved, but use of energy increases
Solution Approach 1:
Machine learning models are pre-trained on large datasets of surgical images to recognize obstructions and surgical sites. This preliminary training allows the models to perform real-time obstruction removal with reduced computational requirements during actual surgical procedures, balancing real-time processing capability with energy consumption.
Data Source
AI summary
An exemplary method of displaying an intraoperative image of a surgical site comprises: receiving a plurality of images captured by a plurality of in-light cameras integrated into one or more surgical light units illuminating the surgical site, wherein the plurality of images capture the surgical site from a plurality of different perspectives; identifying an obstruction to the surgical site in an image of the plurality of images; responsive to identifying the obstruction, generating a composite image based on a set of the plurality of images, wherein the composite image excludes the obstruction; and displaying the composite image as the intraoperative image of the surgical site.