Adversarial Network for Chest X-Ray Organ Segmentation
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Solution Overview
Problem
Current methods for organ segmentation in chest X-rays face challenges due to limited training data, variability in patient populations and imaging quality, and the need to balance local and global structural information, which affects the accuracy of segmentation models.
Innovation Solution
A structure correcting adversarial network (SCAN) is developed, comprising a convolutional segmentation network and a critic network that learns to impose structural regularities, allowing for pixel-level predictions and guiding the segmentation network to generate masks consistent with higher-order structures, even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional multi-stage processing with key point matching is used for organ segmentation, then the method can handle variability in patient populations, but the segmentation accuracy deteriorates due to brittle linear deformations
Solution Approach 1:
The patent replaces the mechanical key point matching and linear deformation system with a neural network-based adaptive deformation field system. The neural network learns non-linear deformation patterns from training data, enabling both high accuracy and adaptability to patient variability without relying on brittle geometric transformations.
Solution Approach 2:
The patent transforms the segmentation approach by changing from fixed geometric parameters (key points and linear transformation parameters) to learned continuous deformation fields represented by neural network weights. This allows the system to adapt to different patient populations while maintaining segmentation accuracy through data-driven parameter optimization.
2Measurement precision
If neural network approaches are used for pixel-level segmentation, then segmentation accuracy improves, but the requirement for large datasets increases which are not readily available
Solution Approach 1:
The patent applies preliminary action by using image-level classification labels (which are more readily available) to pre-train and guide the pixel-level segmentation network. This preliminary supervision provides a head start for the segmentation network, reducing its dependence on large quantities of pixel-level annotated data while still achieving accurate segmentation results.
Solution Approach 2:
The patent introduces image-level classification predictions as an intermediary between the available data and the target pixel-level segmentation task. This intermediary provides coarse guidance that constrains the segmentation network's search space, enabling accurate segmentation with fewer pixel-level annotations.
3Measurement precision
If hand annotation by radiologists is used for organ segmentation, then segmentation accuracy is high, but the workload and time consumption increase significantly
Solution Approach 1:
The patent enables the system to serve itself by using automatically generated image-level classification labels to train the pixel-level segmentation network. This self-service mechanism eliminates the need for expensive and time-consuming manual pixel-level annotations by radiologists, while still achieving high segmentation accuracy through the hierarchical learning approach.
4Measurement precision
If existing segmentation models are used, then initial performance is achieved, but adaptability to different imaging conditions and patient populations deteriorates
Solution Approach 1:
The patent makes the segmentation model dynamic by implementing a two-stage training process where the network first learns from image-level labels and then adapts to pixel-level segmentation. This dynamic training approach enables the model to adapt to different patient populations and imaging conditions while maintaining strong initial performance, unlike static pre-trained models.
Data Source
AI summary
Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.