Medical Image Sequence Segmentation for Stent and Guidewire Visualization
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Solution Overview
Problem
Existing systems lack accurate visualization and efficient processing of medical images, particularly in interventional radiology, leading to challenges in the proper placement of stents and guidewires during coronary artery stenosis treatment.
Innovation Solution
A system utilizing a processor to perform image processing operations, including detection of objects like guidewires and stents, enhanced visualization through neural network models, and image segmentation techniques such as U-Net architecture, to improve accuracy and efficiency in medical imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used for visualizing stents and guidewires, then the processing speed is maintained, but the visualization accuracy and precision are insufficient
Solution Approach 1:
The patent introduces an intermediary processing step that divides the image sequence into multiple sets of images, where each set contains consecutive images. This intermediary structure allows the system to apply complex processing operations selectively to specific image sets rather than the entire sequence, thereby improving visualization accuracy while managing processing complexity through structured segmentation.
Solution Approach 2:
The patent segments the image sequence into multiple sets of images, with each set containing one or more consecutive images. This segmentation enables the system to apply advanced processing techniques to specific segments, improving overall visualization accuracy without requiring complex processing across the entire image sequence, thus balancing precision and complexity.
2Measurement precision
If advanced image processing operations are applied to the entire image sequence, then the visualization quality improves, but the processing time increases
Solution Approach 1:
The patent divides the image sequence into multiple sets of images, allowing the system to apply advanced detection and processing operations to specific sets rather than the entire sequence. This segmentation reduces the total processing time while maintaining high detection precision in critical image sets, effectively balancing quality and efficiency.
Solution Approach 2:
The patent applies advanced image processing operations selectively to specific sets of images rather than uniformly to the entire image sequence. This partial action approach focuses computational resources on image sets that require enhanced processing, thereby achieving high detection precision while minimizing overall processing time.
3Measurement precision
If manual selection of image sets is performed, then the processing accuracy is maintained, but the operational efficiency decreases
Solution Approach 1:
The patent implements automatic detection mechanisms that identify and select image sets containing objects of interest without requiring manual intervention. The system autonomously performs object detection, determines relevant image sets, and processes them accordingly, thereby maintaining high detection precision while significantly improving operational efficiency by eliminating manual selection steps.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system automatically detects objects in image sequences, uses detection results to identify relevant image sets, and applies processing operations based on this feedback. This closed-loop approach maintains high detection precision while improving operational efficiency through automated decision-making that eliminates manual selection.
Data Source
AI summary
Systems and methods for image processing are provided. The method may include obtain an image sequence. The method may also include determine one or more sets of images from the image sequence, wherein each of the one or more sets of images includes one image or a plurality of consecutive images from the image sequence. The method may further include perform an image processing operation on at least one set of the one or more sets of images. The one or more image processing operation may include one or more of image segmentation, visualization, and image enhancement.