GAN Lesion Segmentation for Annotation-Free Size Estimation
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Solution Overview
Problem
Quantifying the size of biological anomalies in medical images is time-intensive and prone to error due to subjective annotation variations, necessitating an automated and accurate method for detection and size estimation.
Innovation Solution
A Generative Adversarial Network (GAN), specifically a Cycle-GAN or Recycle-GAN, is trained to generate fake images without anomalies, allowing for the subtraction of these images from real images to estimate the size of biological anomalies using three-dimensional kernels, with feedback mechanisms to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to quantify the size of biological anomalies, then the process allows for direct measurement, but it is time-intensive and prone to error due to subjectivity
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated image processing system that uses Generative Adversarial Networks (GANs) to generate fake images without anomalies and subtracts them from real images, thereby automatically quantifying anomaly sizes without human intervention
Solution Approach 2:
The system creates a fake copy of the medical image through GAN generation that replicates the real image structure but without the anomaly, allowing automated comparison and measurement by subtracting the fake image from the real image to isolate and quantify the anomaly
2Reliability
If manual annotation is performed to detect biological anomalies, then the process can identify anomaly locations, but it varies due to subjectivity of border locations
Solution Approach 1:
The patent replaces subjective human annotation with an automated GAN-based image processing system that objectively identifies and segments anomalies through mathematical operations, eliminating inter-annotator variability and improving detection consistency
Solution Approach 2:
The system employs adversarial feedback loops where Discriminator networks evaluate the realism of generated images and provide feedback to Generator networks, iteratively improving the accuracy and reliability of anomaly detection and segmentation
3Extent of automation
If a Generative Adversarial Network is trained to generate fake images without anomalies, then automated anomaly size estimation becomes possible, but the training requires large datasets without paired data
Solution Approach 1:
The GAN system generates synthetic copies of medical images with and without anomalies through adversarial training, creating artificial paired data from unpaired real images, thereby enabling automated anomaly detection without requiring large amounts of manually annotated paired training data
Solution Approach 2:
The system performs preliminary generation of fake images without anomalies before the actual anomaly quantification step, preparing the necessary synthetic data in advance to enable subsequent automated subtraction and size estimation operations
Data Source
AI summary
A Generative Adversarial Network (GAN) can be trained, where the GAN includes an anomaly-removing Generator network configured to modify a medical image to remove a depiction of a biological anomaly and one or more Discriminator networks (each configured to discriminate between real and fake images). The anomaly-removing Generator network can then receive a medical image that depicts a particular biological anomaly (or pre-processed version thereof) and generate a modified image predicted to lack any depiction of the particular biological anomaly. The size of the particular biological anomaly may be estimated based on the modified image and the received image (or pre-processed version thereof).