Shape-Prior Segmentation Comparison for Anatomical Abnormality Detection
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Solution Overview
Problem
Medical image analysis for anatomical abnormalities is time-consuming and prone to errors due to dependency on human observers, especially in stressful environments, and existing automated methods struggle to accurately detect minute structures like bone fractures or early-stage cancers.
Innovation Solution
A system utilizing two different segmentation algorithms, one with and one without shape priors, compares their results to enhance detection of anatomical abnormalities by leveraging the strengths of both approaches, particularly using model-based and machine learning-based methods to identify differences indicative of fractures or cancerous tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation algorithms are used to detect anatomical abnormalities, then productivity and time efficiency are improved, but measurement precision and reliability deteriorate due to inability to detect minute structures
Solution Approach 1:
The patent divides the detection task into two separate segmentation algorithms: one using shape priors (model-based) and one without shape priors (data-driven). Each algorithm processes the medical image independently, and their results are compared to identify abnormalities. This segmentation of the detection process allows each algorithm to excel at different aspects, thereby improving overall detection accuracy while maintaining automated efficiency.
2Device complexity
If a single segmentation algorithm is used, then device complexity is reduced, but reliability deteriorates due to inability to compensate for algorithmic biases and errors
Solution Approach 1:
The patent combines the results of two different segmentation algorithms by computing their difference. The first algorithm (with shape priors) provides anatomically constrained segmentation, while the second (without shape priors) provides data-driven segmentation. By merging these complementary approaches and analyzing their differences, the system achieves more reliable detection of abnormalities such as bone fractures and tumors, compensating for the limitations of each individual algorithm.
3Manufacturing precision
If model-based segmentation with shape priors is used, then manufacturing precision and shape accuracy are improved, but adaptability deteriorates due to bias towards expected anatomical shapes
Solution Approach 1:
The patent inverts the traditional approach by using two opposing segmentation strategies: one that enforces shape priors (model-based) and one that does not (data-driven). The abnormalities are detected by inverting the expectation: instead of looking for matches to normal anatomy, the system looks for deviations from the model-based segmentation compared to the data-driven segmentation. This inversion allows detection of atypical anomalies that would be missed by a single approach.
Data Source
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AI summary
A system and related method for image processing. The system comprises an input (IN) interface for receiving two segmentation maps for an input image. The two segmentation maps (11,12) obtained by respective segmentors, a first segmentor (SEG1) and a second segmentor (SEG2). The first segmentor (SEG1) implements a shape-prior-based segmentation algorithm. The second segmentor (SEG2) implements a segmentation algorithm that is not based on a shape-prior, or at least the second segmentor (SEG2) accounts for one or more shape priors at a lower weight as compared to the first segmentor (SEG1). A differentiator (DIF) configured to ascertain a difference between the two segmentation maps. The system may allow detection of abnormalities.