Conditional Diffusion Smoke Removal for Variable Smoke in Laparoscope Images
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Solution Overview
Problem
Traditional smoke removal methods, including machine-based and image processing-based approaches, are inadequate for handling the non-uniform and variable smoke generated during laparoscopic surgery, which affects surgical efficiency and safety.
Innovation Solution
A method using a conditional diffusion model for smoke removal, involving data set creation, forward noise addition, reverse denoising, and multi-loss function fusion, with a smoke sensing module and U-Net network for effective smoke segmentation and concentration extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional machine-based smoke removal methods (suction apparatuses, anti-fogging solutions) are used, then smoke is removed from the surgical field, but the surgery is periodically interrupted, decreasing surgical efficiency
Solution Approach 1:
The patent replaces mechanical smoke removal systems (suction apparatuses, wiping solutions) with an image processing-based deep learning system. The conditional diffusion model processes surgical images in real-time to remove smoke visually, eliminating the need for physical intervention that interrupts surgery. This substitution maintains smoke removal effectiveness while preserving surgical continuity and efficiency.
Solution Approach 2:
The patent creates a virtual copy of the smoke-free surgical field through image processing. Instead of physically removing smoke, the system generates a cleaned version of the smoky image using the conditional diffusion model, allowing surgeons to view a clear representation of the surgical field without interrupting the actual surgical procedure.
2Adaptability or versatility
If traditional single smoke removal models are used, then processing speed is maintained, but the models cannot handle the non-uniform and highly variable smoke in surgical images
Solution Approach 1:
The patent employs a dynamic, adaptive deep learning system rather than a static single model. The conditional diffusion model is trained on diverse surgical smoke data and can adapt to varying smoke conditions (non-uniform distribution, different concentrations, various smoke types) while maintaining reliable smoke removal performance. The system dynamically adjusts to different surgical scenarios.
Solution Approach 2:
The patent uses a composite approach combining multiple components: conditional diffusion model, U-Net architecture, multiple loss functions (L1 loss, perceptual loss, gradient loss), and data augmentation techniques. This composite system provides both adaptability to various smoke conditions and reliability in smoke removal effectiveness, overcoming the limitations of single models.
3Adaptability or versatility
If complex deep learning algorithms are used for smoke removal, then learning capability is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions during the training phase by pre-training the conditional diffusion model on extensive surgical smoke data. This pre-training establishes strong learning capabilities that enable the model to quickly and accurately process real-time surgical images with minimal computational overhead during actual surgical procedures, reducing processing time while maintaining high adaptability.
Data Source
AI summary
Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.