Markerless 3D Body Mesh Visualization for Surgical Outcome Planning
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Solution Overview
Problem
Existing surgical visualization systems for body modifications, such as mammoplasty, are complex, costly, and often fail to accurately predict post-operation outcomes, leading to unmet expectations and dissatisfaction.
Innovation Solution
A computational device using a convolutional neural network processes two-dimensional images of patients to generate three-dimensional models, applying patient-specific parameters to simulate surgical outcomes without the need for tracking markers, enabling real-time augmented or mixed reality visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D scanning equipment and tracking markers are used for surgical visualization, then measurement precision and visualization accuracy are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts and removes the tracking markers and fiducials from the system, replacing them with markerless computer vision techniques. This eliminates the need for physical markers while maintaining visualization accuracy through advanced image processing and 3D reconstruction algorithms.
Solution Approach 2:
The patent replaces the mechanical 3D scanning equipment and physical tracking systems with software-based computer vision and machine learning algorithms. This substitution uses computational methods to achieve 3D reconstruction and surgical outcome visualization without complex mechanical hardware.
2Manufacturing precision
If expensive 3D scanning equipment and specialized devices are used, then manufacturing precision and model accuracy are improved, but ease of operation and accessibility deteriorate
Solution Approach 1:
The patent enables patients to perform self-scanning using their own mobile devices without requiring specialized clinical equipment or professional operators. The system guides patients through the process and automatically performs 3D reconstruction, making the technology accessible for home use.
Solution Approach 2:
The patent designs the system to work with standard mobile device cameras that most patients already possess, rather than requiring specialized scanning equipment. This universal approach allows the same system to function across different devices and settings, from clinical offices to patient homes.
3Measurement precision
If tracking markers and fiducials are placed on the patient's body, then measurement precision is improved, but ease of operation and patient comfort worsen
Solution Approach 1:
The patent completely removes tracking markers and fiducials from the patient's body by using markerless computer vision techniques. The system achieves tracking accuracy through environmental features and body surface analysis without requiring any physical attachments that could cause discomfort or distortion.
4Measurement precision
If specialized depth sensors and clinical equipment are used, then measurement precision is improved, but device complexity and portability worsen
Solution Approach 1:
The patent replaces specialized depth sensors and clinical scanning equipment with standard mobile device cameras combined with computational photography techniques. This substitution uses software-based depth estimation and 3D reconstruction to achieve measurement precision without bulky specialized hardware.
Data Source
AI summary
Embodiments include obtaining an input image depicting a body part of a person and processing the input image against a set of semantic landmarks representing landmarks of the body part; obtaining a mesh model for a set of images; generating, from the mesh model and the set of semantic landmarks, a body part mesh of the person, wherein the body part mesh is an approximation of a 3D model for the body part depicted in the input image; obtaining a target body part mesh data structure, distinct from the body part mesh; and generating a modified view image of the body part, modified to reflect differences between the target body part mesh data structure and the body part mesh while retaining at least some texture of the body part from the input image.