3D Skin Surface Matching for Surgical Navigation Data Selection
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Solution Overview
Problem
The manual selection and assignment of patient-specific image data for surgical navigation is time-consuming and prone to errors, especially when patient identification is difficult, such as in unconscious patients without ID documents.
Innovation Solution
A method and system that utilize three-dimensional shape data comparison through an optical camera system and a shape matching algorithm to automatically or semi-automatically identify patient-specific image data for surgical navigation, reducing errors by comparing geometric data of the patient's skin surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and assignment of patient-specific image data is used, then the process is simple to operate, but it is time-consuming and prone to errors
Solution Approach 1:
The system performs automatic patient identification by comparing 3D geometric data from the patient's skin surface with stored reference data, eliminating the need for manual selection and assignment of image data. The computer system autonomously matches the patient with their corresponding medical image data set through algorithmic comparison of geometric features.
Solution Approach 2:
The manual mechanical process of selecting and assigning patient image data is replaced with an automated optical and computational system. An optical camera system captures 3D geometric data, which is then processed by a computer system using shape matching algorithms to automatically identify and assign the correct image data set, substituting human manual operations with automated technological systems.
2Device complexity
If two-dimensional biometric data are used for patient identification, then the identification process is simplified, but the accuracy and reliability of comparison deteriorates
Solution Approach 1:
The system transitions from using two-dimensional biometric data to three-dimensional geometric data for patient identification. By capturing and comparing 3D shape information of the skin surface, the system achieves more accurate and reliable patient identification while maintaining practical device complexity through the use of optical camera systems and computational algorithms.
3Reliability
If three-dimensional geometric data are used for patient identification, then the accuracy and reliability of data set selection is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system extracts only the essential 3D geometric features of the skin surface that are necessary for patient identification, rather than processing complete volumetric data. This extraction of key geometric characteristics reduces computational complexity and device requirements while maintaining high identification accuracy through focused comparison of relevant shape features.
4Device complexity
If manual patient identification is used, then the system is simpler, but the productivity and efficiency of surgical preparation deteriorates
Solution Approach 1:
The manual patient identification process is replaced with an automated system combining optical camera technology and computer-based shape matching algorithms. This substitution dramatically increases productivity and efficiency by automatically capturing 3D geometric data and rapidly comparing it with stored reference data to identify the correct patient and their associated image data set, eliminating time-consuming manual procedures.
Data Source
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AI summary
A technique for processing patient-specific image data for computer assisted surgical navigation is described. A method realization of this technique comprises providing a database comprising multiple first data sets of two- or three-dimensional image data obtained by a medical imaging method. Each first data set is representative of first shape data or first biometric data of a skin surface of at least one patient. The method further comprises obtaining, by an optical camera system, a second data set of two- or three-dimensional image data of a skin surface of a particular patient and deriving second shape data or second biometric data of the skin surface of the particular patient from the second data set. By comparing the second shape data or second biometric data with the first shape data or the first biometric data of one or more first data sets, a similarity score for each first shape data or first biometric data is calculated. A signal is generated based on the similarity score, wherein the signal triggers one of selection and de-selection of a first data set for computer assisted surgical navigation.