Automatic Key Frame Detection in Ultrasound Imaging
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Solution Overview
Problem
Current medical imaging systems rely heavily on skilled radiologists to select key frames, which is costly and inefficient, as unaided non-radiologist operators may not effectively identify key images for thorough analysis, leading to reduced screening effectiveness and increased healthcare costs.
Innovation Solution
An image processing system that estimates the key image degree of adequacy based on tissue image features and degree of abnormality, automatically selecting key images that satisfy predetermined criteria, allowing non-radiologist operators to perform initial imaging procedures and freeing radiologists for critical tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a highly-trained radiologist performs imaging procedure and selects key frames, then the quality and accuracy of key image selection is improved, but the cost of the imaging procedure increases
Solution Approach 1:
The patent introduces an automated key frame detection system that acts as an intermediary between the imaging procedure and radiologist review. This system uses computer-aided detection algorithms to automatically identify and select key frames from the imaging sequence, providing a cost-effective alternative to having radiologists perform the entire imaging procedure and select key frames manually, while still maintaining adequate image quality for diagnostic purposes
Solution Approach 2:
The imaging system performs self-service by automatically selecting key frames without requiring radiologist intervention for this specific task. The automated system analyzes the imaging sequence in real-time and autonomously identifies frames that meet predetermined adequacy criteria, freeing radiologists from routine key frame selection while maintaining diagnostic quality
2Loss of energy
If un-aided non-radiologist imaging system operators select key frames, then the cost is reduced, but the screening effectiveness decreases
Solution Approach 1:
The patent replaces the mechanical skill-based system of manual key frame selection with an automated computer-aided detection system. Instead of relying on the operator's expertise (which varies), the system uses algorithmic analysis to objectively identify key frames based on image quality metrics and tissue feature detection, providing consistent and reliable results regardless of operator skill level
Solution Approach 2:
The system changes the parameters used for key frame selection from subjective operator judgment to objective, quantifiable image quality parameters. The automated system evaluates frames based on measurable criteria such as tissue feature visibility, image clarity, and anatomical coverage, transforming the selection process into a parameter-driven decision system that improves reliability while reducing costs
3Measurement precision
If radiologists are used for initial imaging procedures and key frame selection, then the accuracy of disease detection is improved, but the availability of healthcare services decreases
Solution Approach 1:
The patent segments the radiologist's role into two distinct functions: (1) automated key frame detection and selection, and (2) final diagnostic interpretation. By dividing these tasks, the system allows automated processing to handle routine key frame identification, while radiologists focus on their core competency of diagnostic analysis, thereby improving overall system productivity and healthcare availability without compromising detection accuracy
Data Source
AI summary
Apparatus and associated methods relate to estimating the key image degree of adequacy of a captured image as a function of tissue image features and a degree of abnormality based on the tissue image features for each image of a real-time series of images captured by an imaging system, and automatically identifying key images based on the key image degree of adequacy. In an illustrative example, the image may be captured by an ultrasound imaging device. The tissue may be, for example, breast tissue examined to detect cancer. In various implementations, the key image degree of adequacy may be determined based on tissue features representative of diseased tissue, and a key image may be selected based on a degree of abnormality determined as a function of the diseased tissue features. Various examples may advantageously provide faster, less expensive, and more accurate initial disease detection by non-radiologist operators.