Spatio-temporal image analysis for medical feature detection
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Solution Overview
Problem
Current video processing and image analysis systems in medical procedures, such as endoscopy, are inefficient in detecting features of interest and recognizing characteristics, leading to potential misdiagnosis due to the manual and time-consuming process of examining large volumes of images.
Innovation Solution
A computer-implemented system that uses spatio-temporal processing modules to analyze images from medical procedures, employing local and global spatio-temporal processing, along with timeseries analysis, to detect and refine features of interest, and generate reports on the likelihood of abnormalities or pathologies, utilizing neural networks for intelligent image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of images is performed, then diagnostic accuracy can be maintained, but examination time increases significantly
Solution Approach 1:
An intelligent detector system acts as an intermediary between the large set of captured images and the physician. The system includes a local spatio-temporal processing module that analyzes subsets of images individually, a global spatio-temporal processing module that processes feature vectors across the entire sequence, and a timeseries analysis module that generates numerical values. This intermediary system pre-processes and flags images containing features of interest, allowing the physician to focus examination on only those flagged images, thus maintaining diagnostic accuracy while dramatically reducing examination time.
2Reliability
If all images are examined manually to ensure accurate diagnosis, then diagnostic precision is maintained, but the process becomes inefficient and error-prone
Solution Approach 1:
The examination process is segmented into two distinct phases: (1) An automated detection phase where the intelligent detector system processes all images through local and global spatio-temporal analysis to identify and flag images containing features of interest; and (2) A focused review phase where the physician examines only the flagged images. This segmentation eliminates the inefficiency of manual review of all images while ensuring that all potentially relevant images are captured, thus maintaining diagnostic precision while improving efficiency.
Solution Approach 2:
The manual mechanical process of examining each image individually is replaced with an automated intelligent detector system that uses spatio-temporal processing and neural networks to automatically identify images containing features of interest. This substitution of mechanical manual examination with an automated detection system eliminates human fatigue and errors associated with reviewing large volumes of images, while the physician's expertise is preserved in the final review of flagged images.
3Quantity of substance
If a large number of images are captured during medical procedures, then comprehensive coverage is achieved, but processing and analysis time increases
Solution Approach 1:
The intelligent detector system performs preliminary action by automatically processing and analyzing all captured images before the physician begins examination. The local spatio-temporal processing module analyzes subsets of images individually, the global spatio-temporal processing module processes feature vectors across the entire sequence, and the timeseries analysis module generates numerical values indicating the presence of features of interest. This preliminary automated analysis identifies and flags only those images containing relevant features, so when the physician reviews images, they are examining a pre-filtered set rather than the entire large collection, thus reducing processing time while maintaining comprehensive coverage.
Data Source
AI summary
A computer-implemented method for detecting at least one feature of interest in images captured with an imaging device includes: receiving an ordered set of images and analyzing one or more subsets of the ordered set using a local spatio-temporal processing module. The local spatio-temporal processing module determines presence of characteristics related to the feature of interest in each image of each subset of images and annotates the subset of images. The method also includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module to refine the determined characteristics associated with each subset of images, and calculate one or more values for each image using a timeseries analysis module, the values being representative of the feature of interest and calculated using the refined characteristics associated with each subset of images and spatio-temporal information.