In Vivo Lumen Contraction Detection via Variable Sequence Segmentation
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Solution Overview
Problem
Current systems for analyzing in-vivo image streams struggle to accurately detect sequences of endoluminal contractions in the gastrointestinal tract due to variations in image capture speed and frame rate, leading to misalignment and missed contractions, especially in cases of irregular motility patterns associated with conditions like IBS.
Innovation Solution
A method that divides the image stream based on lumen size patterns to generate contraction sequences with varying lengths, aligns captured sequences with template sequences by correlating anatomical features, and uses pattern recognition to compare lumen size changes, allowing for accurate detection of contractions regardless of speed disparities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed frame rate and equal-length sequences are used for analysis, then processing simplicity is maintained, but detection accuracy deteriorates due to misalignment with variable-speed captured sequences
Solution Approach 1:
The system dynamically adjusts sequence length based on captured contraction events rather than using fixed equal-length sequences. The processor identifies actual contraction boundaries in the captured video stream and creates variable-length sequences that match the true duration of each contraction event, thereby aligning template sequences with captured sequences of varying lengths to improve detection accuracy
Solution Approach 2:
The system changes the parameter of sequence length from a fixed constant to a variable parameter that adapts to the actual contraction duration. By allowing sequence length to vary based on the captured motility patterns, the system achieves better alignment between template and captured sequences without requiring complex real-time processing adjustments
2Measurement precision
If template sequences are aligned with captured sequences of varying lengths, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system segments the captured video stream into multiple independent contraction sequences based on detected contraction events. By dividing the continuous video stream into discrete, manageable segments corresponding to individual contractions, the processor can efficiently compare each segment against templates without having to process the entire video stream as one large sequence, thereby reducing overall processing time while maintaining accuracy
3Measurement precision
If manual analysis of contraction sequences is performed, then diagnostic accuracy improves, but productivity deteriorates due to time-consuming review processes
Solution Approach 1:
The system performs automatic detection and analysis of contraction sequences through the processor, which independently identifies contractions, creates sequences, compares them against templates, and generates diagnostic information without requiring manual review. This automated self-service approach maintains high diagnostic accuracy while dramatically increasing analysis throughput by eliminating the time-consuming manual review process
Data Source
AI summary
A system and method for comparing captured sequences of in-vivo images with (e.g., template or model) sequences, for example, for computer-automated recognition of contractions. The size of the opening of an in-vivo lumen passageway represented in each frame in a subset of frames of an image stream captured in vivo may be measured. Frames in the subset of frames of the image stream having a local minimum size of the lumen passageway may be identified. The subset of frames may be divided into segments of sequential frames at frames having local maximum lumen sizes before and after the identified frame having a local minimum size of the lumen passageway to generate contraction sequences. A plurality of the contraction sequences may be compared to template sequences. A plurality of the contraction sequences may be displayed.