Neural Image Registration Simulator for Fast Medical Segmentation
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Solution Overview
Problem
Registration of images, particularly in medical imaging, is computationally intensive and lacks reproducibility, while image segmentation is challenging for computers, especially in fields requiring alignment and boundary identification.
Innovation Solution
A neural network is trained using a registration simulator that generates simulated images and displacement fields to align and segment images, utilizing a hybrid loss function combining registration field supervision and data similarity losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image registration methods are used, then image alignment can be achieved, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data through simulation before actual registration is needed. The neural network is pre-trained on simulated images with known displacement fields, so when real registration is performed, the network can quickly predict transformations without intensive computation. This preliminary training phase enables fast inference while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional mechanical/computational image registration methods with a neural network-based system. Instead of using intensive iterative optimization algorithms to align images, the system uses a trained neural network that predicts displacement fields directly, substituting the mechanical computation process with a learned model that provides both speed and accuracy.
2Measurement precision
If manual image segmentation is performed, then accurate boundary identification can be achieved, but it requires significant manual labor and time
Solution Approach 1:
The patent replaces manual segmentation with an automated neural network-based system. The same neural network that performs registration also generates segmentation by predicting displacement fields and applying them to reference segmentations. This substitution eliminates manual labor while maintaining clinical-grade accuracy through the network's ability to learn boundaries from training data.
Solution Approach 2:
The neural network serves multiple functions: it performs both image registration and image segmentation using the same model and training process. By learning displacement fields for registration, the network simultaneously captures boundary information needed for segmentation, eliminating the need for separate manual processes and reducing overall time while maintaining accuracy for both tasks.
3Productivity
If simulated training data is generated, then training efficiency is improved, but the complexity of the training system increases
Solution Approach 1:
The patent introduces a simulation module as an intermediary between data generation and neural network training. This module synthesizes training images by applying known displacement fields to reference images, creating realistic training data without requiring manual annotation. The intermediary simulation layer simplifies the overall system by automating what would otherwise require complex manual data collection and annotation processes.
Data Source
AI summary
Apparatuses, systems, and techniques to perform registration among images. In at least one embodiment, one or more neural networks are trained to indicate registration of features in common among at least two images by generating a first correspondence by simulating a registration process of registering an image and applying the at least two images and the first correspondence to a neural network to derive a second correspondence of the features in common among the at least two images.