Pulmonary Embolism Detection False Positive Reduction
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Solution Overview
Problem
Current pulmonary embolism detection methods, particularly using computed tomography (CT) imaging, face challenges in accurately distinguishing between pulmonary emboli and non-PE structures, leading to high false positive rates, especially in lymph tissue, due to the difficulty in manual inspection and limitations of human attention span and eye fatigue.
Innovation Solution
A system for automatically detecting pulmonary emboli from medical image data involves segmenting airway tracts and artery structures, calculating distances between detected candidates and these structures, and using tensor voting and Hessian-based vesselness scoring to determine the likelihood of candidates being within or outside the artery, with a classifier to reject false positives based on these determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of CT image data is used for PE detection, then diagnostic accuracy may be improved through human expertise, but detection time increases and false positives increase due to human fatigue and attention limitations
Solution Approach 1:
The patent replaces manual visual inspection (mechanical human analysis) with an automated computer system that uses image processing algorithms, distance transforms, and classification models to detect PEs, thereby eliminating human fatigue while maintaining detection accuracy
Solution Approach 2:
The system enables self-service detection where the computer automatically processes CT images, identifies candidate regions, calculates distances to airways, determines arterial location, and classifies PEs without requiring continuous human intervention or review
2Productivity
If automated PE detection is implemented, then detection speed and consistency are improved, but false positive rates increase particularly in lymph/connective tissue regions
Solution Approach 1:
The patent segments the CT image data into distinct anatomical regions (airways, arteries, lymph tissue) and applies region-specific analysis rules, allowing the system to differentiate between PE candidates in arterial locations versus false positives in lymph tissue based on their spatial and structural characteristics
Solution Approach 2:
The system applies different evaluation criteria and classification thresholds to different anatomical regions, with specialized handling for lymph/connective tissue areas where false positives are most common, thereby improving reliability without sacrificing detection speed
3Reliability
If automated detection with multiple analysis steps is used, then false positive reduction is achieved through distance calculation and arterial location determination, but system complexity increases
Solution Approach 1:
The patent performs preliminary segmentation of airways and arteries before PE candidate evaluation, pre-calculating distance maps and arterial location information that are then reused during classification, avoiding redundant computations and managing complexity through staged processing
Data Source
AI summary
A system for automatically detecting pulmonary emboli from medical image data includes receiving image data, automatically detecting one or more pulmonary embolism candidates from the image data, segmenting an airway tract from the image data, segmenting an artery structure from the image data, calculating a distance between each of the candidates and a nearest portion of the segmented airway, determining whether each of the candidates is within or outside of the segmented artery structure, rejecting candidates based on the calculated distance between each of the candidates and the nearest portion of the segmented airway and the determination as to whether each of the candidates is within or outside of the segmented artery structure, and indicating the location of the non-rejected candidates within the image data.