Automated Patient-Specific Anatomy Segmentation for Pathology Measurements

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Solution Overview

Problem

Existing systems and methods for creating 3D models of patient anatomy lack the ability to provide detailed insights into pathological states and anatomical features, limiting their effectiveness in diagnosis, planning, and treatment.

Innovation Solution

A system and method for multi-schema analysis of patient-specific anatomical features from medical images using machine learning algorithms to automatically segment and generate 3D models, incorporating pathological information and physiological parameters for precise decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated segmentation algorithms are used to generate 3D models from medical images, then productivity and speed of analysis are improved, but measurement precision and detection accuracy may deteriorate due to algorithmic errors

Engineering Contradiction:

Improvespeed of 3D model generationVSAvoidaccuracy of anatomical feature identification

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback mechanisms where segmentation results are validated against known anatomical constraints and relationships. The algorithm continuously refines its output by comparing detected features with expected anatomical patterns, allowing automatic correction of segmentation errors while maintaining high processing speed.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary actions by pre-training segmentation algorithms on large datasets of annotated medical images before actual analysis. This pre-training establishes robust feature detection capabilities that maintain high accuracy during automated processing, reducing the need for manual intervention while preserving measurement precision.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If detailed multi-schema analysis is performed to identify specific pathological features, then measurement precision and diagnostic insight are improved, but device complexity and computational requirements increase

Engineering Contradiction:

Improvedetail of anatomical feature analysisVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The complex analysis task is divided into multiple specialized segmentation modules, each targeting specific anatomical structures or pathological features. This modular approach allows the system to achieve high measurement precision for different features independently while managing overall system complexity through organized, reusable components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal analytical frameworks that can handle multiple types of anatomical features and pathologies through a common processing architecture. This multi-functionality reduces device complexity by avoiding the need for separate specialized systems for each analysis type, while still maintaining detailed measurement capabilities across diverse medical applications.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of information

If comprehensive pathological information is extracted from medical images, then information completeness for decision-making is improved, but loss of time for data processing increases

Engineering Contradiction:

Improvecompleteness of pathological dataVSAvoidtime for image analysis

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary identification of potentially relevant pathological features during initial image processing, flagging areas that require detailed analysis. This preliminary action allows the system to focus computational resources on critical regions, maintaining information completeness while reducing overall processing time by avoiding exhaustive analysis of all image data.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a tiered analysis approach where essential pathological information is extracted through streamlined processing, and additional detailed analysis is performed only when clinically indicated. This partial action strategy ensures timely delivery of critical diagnostic information while maintaining the option for more comprehensive analysis when needed, balancing information completeness with processing efficiency.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12444506B2Systems and methods for automated segmentation of patient specific anatomies for pathology specific measurements

Publication Date: 2025.10.14 AXIAL MEDICAL PRINTING LIMITED

AI summary

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.