2D Medical Image Abnormality Detection Using 3D Contour References
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical image analysis by human observers is time-consuming and prone to errors, especially in busy clinical environments, particularly when anatomical abnormalities are not easily discernible.
Innovation Solution
A computer-implemented method using a 3D model to predict 2D contours of anatomical features in 2D medical images, incorporating machine learning for segmentation and classification, to enhance accuracy and efficiency in detecting anatomical abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observers examine medical images for anatomical abnormalities, then diagnostic expertise and experience can be applied, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The patent replaces the mechanical system of human visual inspection with an automated computer-based system that uses machine learning models and image processing algorithms to detect anatomical abnormalities, thereby eliminating time consumption and human error while maintaining high detection accuracy
Solution Approach 2:
The system creates a virtual copy of the anatomical structure by generating a 3D model from 2D medical images, then uses this digital replica to automatically detect abnormalities through comparison and analysis, replacing the need for direct human observation of the original images
2Reliability
If human observers examine medical images, then anatomical abnormalities can be identified, but errors occur depending on expertise, experience, time pressure and exhaustion
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing medical images through embedded algorithms and machine learning models, eliminating dependence on human observers and their varying levels of expertise, thereby ensuring consistent and error-free detection across all cases
Solution Approach 2:
The patent substitutes the human observational process with an automated computational system that applies consistent algorithms and criteria for detecting abnormalities, removing the variable human element that introduces errors due to fatigue, pressure, or varying expertise levels
3Measurement precision
If 2D contours are compared to 3D model reference contours, then anatomical abnormality detection accuracy improves, but system complexity increases
Solution Approach 1:
The system enhances the 2D medical image analysis by introducing a 3D dimensional perspective through virtual modeling, allowing contours to be compared across multiple dimensions and planes, which significantly improves detection accuracy by revealing abnormalities that may not be apparent in single 2D views
Solution Approach 2:
The patent introduces a virtual 3D model as an intermediary between the raw 2D medical images and the final abnormality detection process, serving as a reference framework that mediates the comparison and analysis, thereby simplifying the overall system architecture while improving measurement precision
Data Source
Figure 1
Figure 2A~2C
Figure 3
AI summary
The invention relates to detecting anatomical abnormalities in medical images. In order to detect anatomical abnormalities, a computer-implemented method (100) and system are disclosed that detect 2D contours (130) of anatomical features in a medical image and compares these contours with predicted 2D contours (140) based on a 3D reference model in order to detect (150) anatomical abnormalities. This approach may improve accuracy of anatomical abnormality detection, thereby cutting time in a medical facility and potentially improving operator experiences and patient outcomes.