Patient-Specific Anatomy Segmentation for Accurate 3D Pathology Models

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Solution Overview

Problem

Existing systems fail to provide accurate 3D models of patient-specific anatomy for enhanced diagnosis, planning, and treatment, lacking the ability to distinguish between normal and pathological states and requiring improved methods for segmentation and analysis of medical images.

Innovation Solution

A system and method for multi-schema analysis of patient-specific anatomical features using machine learning algorithms to segment and generate 3D models from medical images, incorporating neural networks and reinforcement learning to identify anatomical features and landmarks, enabling precise measurements and pathology-specific interventions.

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 reliability of anatomical feature identification 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 initial segmentations with expected anatomical patterns, correcting errors automatically 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 use. This preliminary training establishes robust patterns that enable accurate identification during automated processing, reducing the need for manual correction while maintaining speed.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If detailed multi-schema analysis is performed to distinguish normal and pathological states, then measurement precision and diagnostic accuracy are improved, but device complexity and computational requirements increase

Engineering Contradiction:

Improveaccuracy of pathology identificationVSAvoidcomplexity of analysis system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The complex analysis task is segmented into multiple specialized algorithms, each handling specific anatomical structures or pathological conditions. This modular approach allows the system to achieve high diagnostic accuracy through coordinated simple operations rather than one complex monolithic process, managing computational complexity effectively.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal data structures and processing frameworks that can handle multiple types of anatomical features and pathological conditions through the same underlying architecture. This multi-functionality reduces overall system complexity by avoiding separate specialized systems for each analysis type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Loss of information

If comprehensive 3D modeling of complete anatomy is generated, then information completeness and diagnostic insight are improved, but loss of time and computational resources increase

Engineering Contradiction:

Improvecompleteness of anatomical informationVSAvoidprocessing time for full anatomy analysis

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system applies local quality by generating detailed 3D models only for anatomical regions relevant to the specific clinical question or pathology being investigated. Rather than uniformly processing the entire anatomy, it focuses computational resources on areas of interest while maintaining comprehensive information where needed, reducing overall processing time.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs partial action by generating 3D models at different levels of detail based on clinical requirements. For routine assessments, simplified models are generated quickly, while more detailed comprehensive models are created only when clinically necessary, balancing information completeness with processing efficiency.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260031238A1Systems and methods for automated segmentation of patient specific anatomies for pathology specific measurements

Publication Date: 2026.01.29 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.