Automated Bone Scan Metastasis Detection System

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Solution Overview

Problem

Interpreting medical images, particularly determining human skeleton contours and detecting cancer metastases, is a time-consuming and error-prone process that requires manual steps, necessitating an automated method for efficient interpretation.

Innovation Solution

A system comprising a shape identifier unit, hotspot detection unit, hotspot feature extraction unit, and artificial neural networks to automatically detect and analyze bone scan images, reducing manual work and creating a comparable atlas image for interpreting bone cancer metastases.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual interpretation of bone scan images is used, then interpretation accuracy can be maintained, but time consumption increases and error rates increase

Engineering Contradiction:

Improveinterpretation accuracyVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical interpretation with an automated computer-based system that uses image processing algorithms and neural networks to detect bone metastases, thereby reducing time consumption while maintaining diagnostic accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-diagnosis by automatically analyzing bone scan images, identifying anatomical structures, detecting hotspots, and generating interpretations without requiring continuous manual intervention, thus reducing time loss while maintaining reliability

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual contour determination of skeleton and metastases is performed, then detection precision can be achieved, but the process becomes labor intensive and time consuming

Engineering Contradiction:

Improvedetection precisionVSAvoidprocessing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual contour determination with automated image processing techniques including threshold-based hotspot detection, anatomical structure identification algorithms, and neural network-based analysis, achieving both high detection precision and processing efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system segments the bone scan image into anatomical regions and identifies distinct contours of skeleton and metastases separately through automated algorithms, enabling precise detection while improving productivity by processing multiple regions simultaneously

Inventive Principle:

Principle #1Segmentation

3Productivity

If automated interpretation system is implemented, then processing speed increases, but system complexity increases

Engineering Contradiction:

Improveprocessing speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the complex automated interpretation system into separate functional modules: anatomical structure identification module, hotspot detection module, feature extraction module, and neural network analysis module, making the system more manageable and easier to implement while maintaining high processing speed

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediate processing layers including feature extraction units and anatomical region mapping that bridge raw image data and final diagnosis, simplifying the overall system architecture while maintaining automated processing capability and speed

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUSRE47609E1System for detecting bone cancer metastases

Publication Date: 2019.09.17 EXINI DIAGNOSTICS

AI summary

The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.