Neural Network Tissue Segmentation and Bounding Box Merging
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Solution Overview
Problem
Current methods for measuring human body tissue from medical images are manual, repetitive, and prone to errors and variability among radiologists, limiting their accuracy and efficiency.
Innovation Solution
A computer-implemented method using a trained neural network to identify and segment human body tissue from medical images, compute bounding boxes, determine intersections, and merge segments to accurately measure tissue size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement by radiologists is used, then expertise-based interpretation is achieved, but measurement errors and variability between experts occur
Solution Approach 1:
The system enables self-service measurement by automatically processing medical images through neural networks and algorithms, eliminating the need for manual radiologist interpretation. The computer-implemented method independently performs segmentation, bounding box computation, and measurement calculation without human intervention, thereby removing inter-expert variability while maintaining measurement accuracy.
Solution Approach 2:
The patent replaces the mechanical human interpretation process with an automated computational system. Neural networks and algorithms substitute for radiologist expertise, performing image analysis, segmentation, and measurement tasks that were previously done manually. This substitution eliminates human error and inconsistency while maintaining or improving measurement precision.
2Reliability
If manual measurement processes are used, then radiologist expertise is utilized, but the process becomes repetitive and tedious
Solution Approach 1:
The system performs self-service by automatically executing the complete measurement workflow from image input to final measurement output. The computer-implemented method handles segmentation, bounding box computation, intersection determination, and measurement calculation without requiring radiologist intervention, thereby eliminating repetitive manual tasks while maintaining measurement quality.
Solution Approach 2:
The system performs preliminary actions by pre-processing images through neural network segmentation and pre-computing bounding boxes before final measurement. This automated preparation work eliminates the need for radiologists to perform repetitive preliminary analysis, freeing them from tedious tasks while ensuring consistent quality through algorithmic precision.
3Productivity
If automated approaches like maskrcnn are used, then productivity is improved, but specialized models per organ are required increasing complexity
Solution Approach 1:
The patent implements a universal measurement framework that can process multiple organ types and imaging modalities through a single computer-implemented method. The system uses general-purpose neural networks and algorithms that adapt to different organs (liver, kidney, spleen, etc.) and image types (CT, MRI, ultrasound) without requiring separate specialized models, thereby maintaining high productivity while reducing system complexity.
Solution Approach 2:
The system applies segmentation to divide the complex task of multi-organ measurement into distinct processing stages: image segmentation, bounding box computation, intersection determination, and measurement calculation. This segmented approach allows a single universal model to handle diverse organs efficiently by processing each through the same standardized pipeline, reducing the need for organ-specific specialized models.
4Measurement precision
If radiologists manually select best practice images, then measurement accuracy is maintained, but time consumption increases
Solution Approach 1:
The system performs self-service by automatically selecting and processing optimal images without radiologist intervention. The computer-implemented method independently evaluates multiple images, determines the best practice image for measurement, and proceeds with automated analysis, thereby eliminating the time radiologists spend on manual image selection while maintaining measurement accuracy through algorithmic optimization.
Solution Approach 2:
The system performs preliminary image evaluation and selection automatically before the measurement process. Neural networks pre-analyze multiple images to identify the optimal image for measurement based on quality metrics and anatomical clarity, eliminating the need for radiologists to manually review and select images, thereby saving time while ensuring measurement accuracy is maintained.
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method for measuring a human body tissue from a set of medical images representing the human body tissue. The method comprises obtaining a trained neural network configured for outputting segments of human body tissue. The method also comprises applying the trained neural network to the set. The method thereby identifies one or more segments of human body tissue for at least two images of the set. The method also computes a bounding box enclosing each segment. The method also determines an intersection between a pair of bounding boxes. If the intersection of the pair is non-empty, the method determines an intersection between the segments. If the intersection between the segments is non-empty, the method merges the segments by computing a resulting bounding box enclosing the segments. The method also measures the size of the segments comprised in the resulting bounding box.