3D Organ Reconstruction from Sparse 2D Images Using Neural Implicit Shapes
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Solution Overview
Problem
Three dimensional shape reconstruction from two dimensional sparse images is challenging due to the time-consuming nature of existing methods requiring high anatomical expertise and is inefficient in reconstructing organs from sparse data.
Innovation Solution
A method involving boundary detection using deep learning to generate organ contours followed by neural implicit shape function modeling to regress a three dimensional organ shape, utilizing a neural implicit shape function model to predict signed distances and generate three dimensional organ shapes from sparse two dimensional medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual annotation methods are used to reconstruct three dimensional organs from two dimensional images, then reconstruction accuracy can be maintained, but the process becomes time-consuming and requires high anatomical expertise
Solution Approach 1:
The system performs automatic boundary detection and organ contour generation without requiring manual annotation by clinical specialists. The deep learning model processes two dimensional images autonomously to extract organ boundaries and generate three dimensional reconstructions, eliminating the time-consuming manual expertise requirement while maintaining accuracy through learned patterns from training data
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated deep learning-based boundary detection. The neural network models automatically identify organ boundaries in two dimensional images and reconstruct three dimensional shapes, substituting human expert manual work with computational algorithms that achieve comparable or superior efficiency
2Productivity
If traditional methods are used to reconstruct organs from sparse two dimensional data, then reconstruction can be performed, but the process requires high anatomical expertise and is inefficient
Solution Approach 1:
The deep learning system autonomously performs boundary detection and three dimensional reconstruction from sparse two dimensional images without requiring user expertise. The models automatically interpret anatomical structures, detect boundaries, and generate reconstructions independently, transforming a complex expertise-dependent process into an automated self-service system
Solution Approach 2:
The patent transforms the input data representation from sparse two dimensional pixel data to processed contour data and then to three dimensional shape representations. The deep learning models operate on transformed parameters (boundary coordinates, contour data) rather than raw sparse images, enabling efficient reconstruction by working with intermediate representations that capture essential anatomical information
3Measurement precision
If complete contour data is required for three dimensional reconstruction, then reconstruction accuracy is maintained, but the method cannot efficiently handle sparse imaging data
Solution Approach 1:
The deep learning models are trained to perform accurate boundary detection and three dimensional reconstruction using only partial contour data from sparse two dimensional images. Rather than requiring complete anatomical contours, the models learn to infer missing structures from available partial observations, achieving accurate reconstructions from incomplete data through learned anatomical priors
Solution Approach 2:
The patent introduces intermediate representation layers including boundary detection outputs and organ contour data that serve as mediators between sparse two dimensional images and final three dimensional reconstructions. These intermediate representations capture essential anatomical information from limited views and enable accurate reconstruction by bridging the gap between incomplete input data and complete output models
Data Source
AI summary
Systems and methods for three dimensional reconstruction from two dimensional imaging data. An neural implicit shape function model is used to regress a shape of an organ from two dimensional sparse imaging data. Contours of the organ are generated from the two dimensional sparse imaging data. The contours are used to train the neural implicit shape function model to regress the shape of the organ.