Surgical Video Style Transfer for Consistent Intraoperative Visualization
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Solution Overview
Problem
Medical imaging systems in minimally invasive surgery often produce inconsistent image styles due to variations in cameras and display systems, which can interfere with surgeons' workflow and decision-making, particularly during intraoperative events like bleeding.
Innovation Solution
A system and method for modifying the style of surgical video streams or images based on a surgeon's preferences using neural networks to convert the style of images in real-time or post-operatively, allowing for consistent visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different cameras and display systems are used in medical imaging, then device versatility and availability are improved, but image style consistency deteriorates
Solution Approach 1:
The patent introduces a style transfer model as an intermediary between the captured surgical video and the displayed video. This model receives input from various camera systems and applies learned style transformations to produce consistent output across different devices, effectively mediating the inconsistency caused by hardware variations
Solution Approach 2:
The system changes the style parameters of the video output by using a trained neural network model that learns to transform images from different source styles into a target style. The model modifies parameters such as color distribution, brightness, and contrast to achieve visual consistency across different camera and display systems
2Ease of operation
If image style is adjusted to match surgeon preferences, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The style transfer model is trained in advance using datasets of surgical videos from multiple sources. This preliminary training phase allows the model to learn the transformations needed to achieve consistent styles, so that during actual surgical procedures, the style adjustment happens automatically without requiring real-time manual configuration or complex device setup
Solution Approach 2:
The system performs self-service by automatically applying the appropriate style transformations based on the input video source. The trained model autonomously identifies and corrects style inconsistencies without requiring manual intervention from the surgeon or operator, thereby improving ease of operation while the complexity is contained within the automated processing system
Data Source
AI summary
Disclosed herein are systems and methods configured to modify a style of a video stream or one or more images of a target area of a subject. The style of the video stream or image(s) may be modified according to user input, e.g., a selection of a reference style indicative of a surgeon's preferences for visualizing the target area of the subject, such as during or after a procedure. The reference style may be a fixed reference style stored in memory, or a matched reference style. The video stream and/or image(s) may be captured using a video camera. The system may generate a modified video stream and/or modified image(s) of the target area of the subject to be displayed. The modified video stream or image(s) may include the content of the original video stream or image(s) captured by a camera (e.g., laparoscopic camera) and the style of the surgeon's preferences.