3D Organ Shape Reconstruction from 2D Topograms Using Generative Networks
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Solution Overview
Problem
Current methods for reconstructing 3D organ shapes from 2D X-ray images, such as topograms, face challenges due to visual ambiguities and information loss from projection, leading to inaccurate and complex shape delineation, especially when dealing with heterogeneous organs like the liver.
Innovation Solution
A machine-learned generative network is trained using a topogram encoder and decoder, with optional 3D shape and mask encoders, to infer 3D shape predictions from 2D topograms, providing a more accurate and detailed reconstruction of organ shapes by learning complex shape variations and spatial context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT scan is used to obtain 3D organ shape information, then measurement precision is improved, but loss of time and cost increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that translates 2D topogram images into 3D organ shape predictions. This mediator bypasses the need for direct CT scanning, achieving accurate 3D reconstruction from simpler 2D projections while reducing time and cost requirements.
Solution Approach 2:
The system creates a virtual copy of the 3D organ shape by inferring it from 2D topogram images through trained neural networks. This digital replica captures essential geometric information without requiring actual 3D volumetric scanning, thereby reducing examination time and radiation exposure.
2Measurement precision
If CT scan is used to obtain 3D organ shape information, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The machine learning model serves as an intermediary that derives 3D shape information from 2D topogram images rather than directly from CT volumetric data. This approach eliminates the need for patients to receive ionizing radiation during the imaging process while still achieving accurate 3D organ reconstruction through computational inference.
3Loss of time
If 2D topogram is used to delineate 3D organ shape, then loss of time is reduced, but measurement precision deteriorates
Solution Approach 1:
The system changes the fundamental parameter of data representation by transforming 2D topogram images into 3D shape predictions through machine learning. This parameter transformation enables the system to maintain the speed advantage of 2D imaging while achieving 3D reconstruction accuracy that was previously unattainable from 2D data alone.
Solution Approach 2:
The patent replaces traditional mechanical image processing methods with machine learning-based inference. Instead of using complex algorithms to manually interpret 2D projections, the system employs trained neural networks that automatically convert 2D topograms into accurate 3D organ shape representations, significantly improving both speed and accuracy.
4Device complexity
If traditional segmentation methods are used on 2D topograms, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system replaces traditional mechanical image processing algorithms with machine learning models. The neural networks are pre-trained on extensive datasets of 3D organ geometries and automatically learn to infer accurate 3D shapes from 2D topograms, achieving superior precision without requiring complex manual segmentation procedures.
Data Source
AI summary
A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.