Automated 3D Tissue Modeling for Surgical Planning
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Solution Overview
Problem
Current surgical planning techniques are inefficient and prone to subjective errors due to manual processing of CT and MRI images, particularly in orthopedic procedures where accurate modeling of multiple tissue types is necessary.
Innovation Solution
A system and process for multi-tissue 3D modeling that utilizes a rule-based method for weak annotation, integrates multi-scale image features and anatomical priors, and employs a comprehensive loss function for Convolutional Neural Network (CNN) training to optimize pixel classification and feature distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual surgical planning is conducted by surgeons based on CT and MRI images, then surgical planning can be performed with human judgment and experience, but the process takes relatively long time and reduces treatment efficiency
Solution Approach 1:
The patent replaces the manual mechanical process of surgical planning with an automated computer-based system. The system automatically generates 3D models from medical images and provides surgical guidance, substituting the surgeon's manual measurement and planning processes while maintaining decision-making authority with the surgeon.
Solution Approach 2:
The system enables self-service by automatically processing medical images to generate 3D models and surgical plans without requiring manual intervention for each planning step. The automated image processing and model generation allow the system to serve itself in preparing surgical data, reducing the time burden on surgeons.
2Reliability
If manual surgical planning is conducted by surgeons, then human expertise can be applied to complex cases, but inter-rater variations occur that may influence success rate
Solution Approach 1:
The system changes the parameters of surgical planning from subjective human measurements to objective digital parameters. By converting anatomical structures into standardized 3D models with precise coordinates and dimensions, the system eliminates variability between different surgeons' measurements while maintaining clinical relevance.
Solution Approach 2:
The system creates accurate digital copies of the patient's anatomy from medical images. These 3D models serve as faithful reproductions of the actual anatomical structures, allowing surgeons to plan surgery on the digital copy with the same reliability as manual planning but without inter-rater variations.
3Adaptability or versatility
If CT images are used for surgical planning, then bone structure can be visualized, but the ability to provide surgical planning for soft tissue is lacking
Solution Approach 1:
The system achieves multi-functionality by being able to process multiple types of medical images (CT and MRI) and generate 3D models for different tissue types. The same computational framework adapts to different imaging modalities, making the system universally applicable to both bone and soft tissue surgical planning.
Solution Approach 2:
The system uses composite information from different imaging modalities. By integrating CT data for bone structures and MRI data for soft tissues, the system creates a comprehensive 3D model that combines information from multiple sources, similar to how composite materials combine properties of different materials.
Data Source
AI summary
A process for tissue modelling from a medical image of a subject, for forming a three-dimensional (3D) model of a region of interest (ROI) of a subject with one or more tissue types, said process including the steps of (i) utilising a rule-based method that automatically generates the weak annotation, initial seed area from a medial image (210b); (ii) utilising a proposal generation method that integrates the multi-scale image features and anatomical prior (220b); and (iii) a comprehensive loss for CNN training that optimizes the pixel classification and feature distribution simultaneously (230).