Automated Chest X-Ray Quality Assessment via Rib-Lung Overlap
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Solution Overview
Problem
Current chest X-ray imaging methods are inefficient due to manual evaluation of breath-holding state, leading to poor imaging quality and low processing speed, as technicians must count overlapping ribs, a time-consuming task.
Innovation Solution
A method using machine-learned rib and lung field segmentation models to automatically determine if specific ribs overlap with lung fields in chest X-ray images, assessing image quality and eliminating the need for manual evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of rib overlap is used to determine image quality, then measurement precision is maintained, but productivity deteriorates due to time-consuming manual counting
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated computer-based system that uses image processing algorithms to detect and count rib overlaps. The system automatically identifies rib structures and lung fields in chest X-ray images, calculates overlap metrics, and determines image quality without human intervention, thereby maintaining measurement precision while dramatically improving processing speed.
2Reliability
If manual evaluation by technicians is used, then reliability of quality assessment is maintained, but loss of time increases due to large volume of images requiring review
Solution Approach 1:
The system enables self-service quality assessment by automatically evaluating chest X-ray images without requiring technician intervention. The automated algorithm performs rib overlap detection, quality metric calculation, and image acceptance/rejection determination, allowing the imaging system to assess its own output quality instantly while maintaining reliable assessment standards.
3Productivity
If automated processing is implemented, then productivity is improved, but device complexity increases due to need for machine-learned segmentation models
Solution Approach 1:
The patent introduces an intermediary automated processing system that acts as a mediator between image acquisition and quality assessment. This intermediary system uses pre-trained machine learning models for rib segmentation and lung field segmentation, which process images through standardized algorithms to generate quality metrics, thereby improving productivity while managing complexity through modular, pre-configured processing steps.
Data Source
AI summary
Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.