Patient 3D Model Reconstruction from Video Sequences
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Solution Overview
Problem
Conventional methods for generating representations of a patient's body in medical environments fail to accurately depict the patient's shape and pose, particularly when body parts are occluded or missing, affecting precision in medical procedures like radiation therapy and imaging.
Innovation Solution
Systems and methods utilizing processors to generate and adjust 2D or 3D representations of patients based on video sequences, employing machine-learning models, such as convolutional neural networks, to reconstruct missing body parts and improve accuracy by combining multiple observations from different image subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to generate body representations, then the process is simple and fast, but the accuracy of body shape and pose representation deteriorates, especially when body parts are occluded
Solution Approach 1:
The system performs preliminary actions by capturing multiple images from different viewpoints before the representation is finalized. This allows the system to have all necessary visual information available before generating the body representation, ensuring complete data for accurate reconstruction even when body parts are occluded in any single view.
Solution Approach 2:
The system transitions from two-dimensional image data to a three-dimensional body representation. By converting 2D images into a 3D mesh model, the system recovers depth information and spatial relationships, enabling accurate representation of body shape and pose that cannot be achieved with 2D images alone.
2Measurement precision
If multiple images from different viewpoints are captured and processed, then the accuracy of body representation improves, but the processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing multiple images from different viewpoints before the representation is finalized. This allows the system to have all necessary visual information available before generating the body representation, ensuring complete data for accurate reconstruction even when body parts are occluded in any single view.
Solution Approach 2:
The system creates a simplified 3D mesh model that copies and represents the essential geometric features of the body from multiple 2D images. This mesh model serves as a compact representation that captures the body's shape and pose without requiring processing of all original image data, reducing computational burden while maintaining accuracy.
3Productivity
If a single 2D or 3D representation is generated from limited images, then the processing is faster, but body parts that are occluded or missing are not accurately represented
Solution Approach 1:
The system performs preliminary actions by capturing multiple images from different viewpoints before the representation is finalized. This allows the system to have all necessary visual information available before generating the body representation, ensuring complete data for accurate reconstruction even when body parts are occluded in any single view.
Solution Approach 2:
The system merges information from multiple images taken from different viewpoints to create a comprehensive body representation. By combining the data from all images, the system reconstructs complete body geometry including occluded body parts, achieving both speed and completeness through integrated processing.
Data Source
AI summary
A video sequence depicting a person in a medical environment may be obtained and used for determining one or more human models of the person. A first human model representing a first pose or a first body shape of the person may be determined based on a first subset of images from the video sequence, while a second human model representing a second pose or a second body shape of the person may be determined based on a second subset of images from the video sequence. The second 2D or 3D representation of the person may include an adjustment to the first 2D or 3D representation of the person based on the observation of the person provided by the second subset of images.