Spatio-Temporal Image Analysis for Faster Medical Feature Detection
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Solution Overview
Problem
Existing video processing and image analysis systems struggle to accurately detect and analyze features of interest in medical procedures, such as endoscopy and capsule endoscopy, leading to inefficiencies and potential misdiagnoses due to the manual review of large image sets.
Innovation Solution
A computer-implemented system using spatio-temporal image analysis with local and global processing modules, recurrent neural networks, and temporal convolution networks to intelligently process images, detect features of interest, and generate reports on their likelihood and characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of large image sets is performed, then diagnostic accuracy can be maintained through human expertise, but time consumption and inefficiency increase significantly
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the large set of captured images and the physician. This system processes images using spatio-temporal analysis, recurrent neural networks, and temporal convolution networks to identify features of interest, thereby reducing the time burden on physicians while maintaining diagnostic accuracy through automated detection and reporting capabilities.
2Productivity
If automated image processing is implemented, then efficiency and speed of analysis improve, but accuracy and reliability of feature detection may deteriorate
Solution Approach 1:
The patent divides the image analysis task into multiple specialized modules: spatio-temporal analysis for extracting spatial and temporal features, recurrent neural networks for sequence modeling, and temporal convolution networks for temporal pattern recognition. Each module focuses on specific aspects of feature detection, improving overall accuracy while maintaining high processing efficiency through divided computational responsibilities.
3Measurement precision
If all captured images are analyzed in detail, then comprehensive detection of features of interest is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most relevant features from the large set of captured images using spatio-temporal analysis. Instead of analyzing all images in equal detail, the system identifies and focuses computational resources on images containing features of interest, such as lesions or abnormalities, thereby reducing computational complexity while maintaining detection completeness.
4Measurement precision
If frame rate of capsule device is increased to capture more images, then diagnostic information quality improves, but data volume and processing burden increase
Solution Approach 1:
The patent performs preliminary spatio-temporal analysis and feature extraction on the large volume of images captured at high frame rates. By pre-processing images to identify and extract relevant features before detailed examination, the system maintains high diagnostic information quality from the increased data volume while reducing the processing burden through early filtering and feature selection.
Data Source
AI summary
A computer-implemented method is provided for detecting at least one feature of interest in images captured with an imaging device. The method includes receiving an ordered set of images from the captured images, the ordered set of images being temporally ordered and analyzing one or more subsets of the ordered set of images using a local spatio-temporal processing module, the local spatio-temporal processing module being configured to determine the presence of characteristics related to the at least one feature of interest in each image of each subset of images and to annotate the subset of images based on the determined characteristics in each image of each subset of images. The method further includes processing a set of feature vectors of the ordered set of images using a global spatio-temporal processing module, the global spatio-temporal processing module being configured to refine the determined characteristics associated with each subset of images, and calculating one or more values for each image using a timeseries analysis module, the numerical value being representative of the at least one feature of interest and calculated using the refined characteristics associated each subset of images and spatio-temporal information. Still further, the method may include generating a report, a data or electronic file, integration into another reporting system or electronic medical records, and/or generating an electronic display on the at least one feature of interest using the multiple values associated with each image of each subset of the ordered set of images.