Smart Image Editing via Iterative Machine Learning Models
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Solution Overview
Problem
In clinical settings, especially in interventional and sterile environments, clinicians lack access to advanced editing tools for image processing tasks, leading to inefficiencies and disruptions in workflow due to the need for manual or semi-automatic editing using devices like mice or styluses, which are not always feasible.
Innovation Solution
A computer-implemented method using iterative machine learning models to generate and present multiple processed images to users for acceptance or rejection, allowing for smart editing without the need for manual manipulation of images, with a second machine learning model trained to refine options based on user feedback for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automatic editing tools (mouse, stylus) are used for image processing, then editing precision can be achieved, but workflow efficiency deteriorates and inter-user variability increases
Solution Approach 1:
The patent replaces manual mechanical input devices (mouse, stylus) with an automated machine learning-based editing system. The system uses trained models to automatically perform contour editing tasks that previously required manual user input, thereby eliminating the trade-off between precision and efficiency by removing human variability and time consumption while maintaining high accuracy through algorithmic consistency.
Solution Approach 2:
The patent implements self-service automation where the machine learning model performs editing tasks autonomously without requiring manual user intervention. The system processes images, generates edited versions, and outputs results automatically, allowing the workflow to continue without waiting for manual editing while maintaining consistent quality through the trained model's standardized processing approach.
2Adaptability or versatility
If advanced editing tools are made available, then editing capability is improved, but device complexity and accessibility worsen in clinical environments
Solution Approach 1:
The patent extracts the complex editing functionality from traditional workstations and integrates it directly into the clinical imaging system's workflow. By embedding the machine learning editing capabilities within the existing clinical environment, the system provides advanced editing functionality without requiring separate complex devices or additional hardware, thus maintaining simplicity and accessibility while enhancing capability.
3Measurement precision
If manual editing is performed in sterile interventional environments, then editing accuracy can be achieved, but workflow disruption increases
Solution Approach 1:
The patent enables continuous workflow by performing automated editing operations that do not interrupt the clinical procedure. The machine learning model processes images in the background or during transitions without requiring the clinician to stop and perform manual editing, maintaining both accuracy through automated consistency and workflow continuity by eliminating interruptions in sterile environments.
Data Source
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AI summary
A computer-implemented method for editing image processing results includes performing one or more image processing tasks on an input image using an iterative editing process. The iterative editing process is executed until receiving a user exit request. Each iteration of the iterative editing process comprises using a first machine learning model to generate a plurality of processed images. Each processed image corresponds to a distinct set of processing parameters. The iterative editing process further comprises presenting the plurality of processed images to a user on a display and receiving a user response comprising (i) an indication of acceptance of one or more of the processed images, (ii) an indication of rejection of all of the processed images, or (iii) the user exit request. Following the iterative editing process clinical tasks are performed using at least one of the processed images generated immediately prior to receiving the user exit request.