GAN Lesion Segmentation Without Manual Annotation
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Solution Overview
Problem
Manual annotation of biological anomalies in medical images is time-intensive and prone to errors due to subjectivity, making it difficult to accurately quantify their size and position.
Innovation Solution
A Generative Adversarial Network (GAN), specifically a Cycle-GAN or Recycle-GAN, is trained to generate fake images without anomalies, allowing for automated segmentation and size estimation by subtracting fake images from real ones, using Discriminator networks for feedback and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to detect and quantify biological anomalies, then annotators can mark outlines of anomalies on images, but the process is time-intensive and prone to error due to subjectivity of border locations
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated computational system using Generative Adversarial Networks. The GAN automatically segments anomalies and estimates their sizes by generating and comparing synthetic images, eliminating the need for time-consuming manual outlining while improving measurement precision through objective algorithmic boundary detection
Solution Approach 2:
The patent uses Generative Adversarial Networks to create synthetic copies of medical images with annotated anomalies. The GAN generates fake images that mimic real medical images, allowing automated training and segmentation without requiring manual annotation of every training image, thus reducing time loss while maintaining measurement accuracy
2Reliability
If manual annotation is used to detect biological anomalies, then anomalies can be identified on images, but the process is prone to error as a result of variation across annotations
Solution Approach 1:
The patent replaces variable human annotation with a consistent automated GAN-based system. The neural network applies the same segmentation algorithms and boundary detection criteria uniformly across all images, eliminating inter-annotator variability and improving reliability of anomaly detection results
Solution Approach 2:
The patent implements feedback loops within the GAN architecture where Discriminator networks evaluate generated images and provide feedback to Generator networks for iterative improvement. This feedback mechanism ensures consistent application of segmentation criteria and enables the system to learn from errors, improving reliability while managing complexity through structured learning processes
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).