Neural Network Image Segmentation Using Disentangled Feature Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image segmentation methods require manually labeled training data, which is time-consuming and often requires expert input, especially for medical imaging, limiting their accessibility and efficiency.
Innovation Solution
A neural network is trained using weakly-labeled data that includes images with and without a specific feature of interest, employing an encoder, common decoder, and residual decoder to disentangle unique and common features, allowing for the generation of segmentation masks without the need for complex manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to generate training data, then segmentation accuracy can be achieved, but time consumption and expert dependency increase significantly
Solution Approach 1:
The patent uses synthetic training data generated by copying and transforming existing medical images through automated processing. Instead of manual labeling, the system creates artificial segmentation examples by applying geometric transformations, intensity modifications, and noise additions to source images, thereby eliminating time-consuming manual annotation while preserving segmentation accuracy
Solution Approach 2:
The system enables self-service by allowing the training data generation process to be performed automatically without expert intervention. The automated pipeline includes image preprocessing, synthetic label generation, and quality validation steps that operate independently, removing dependency on expert annotators while maintaining data quality sufficient for accurate segmentation
2Measurement precision
If expert-generated labels are used for training, then segmentation quality improves, but accessibility and efficiency deteriorate due to expert availability constraints
Solution Approach 1:
The patent replaces expert-generated labels with synthetic labels created by copying image features and applying automated segmentation algorithms. This copying approach generates sufficient training examples without requiring expert time, thereby improving productivity and accessibility while maintaining segmentation quality through careful synthesis parameter optimization
Solution Approach 2:
The system achieves universality by creating a general-purpose training data generation pipeline that can produce synthetic labeled data for various medical imaging modalities and segmentation tasks. This multi-functional approach eliminates the need for domain-specific expert labeling while maintaining segmentation quality across different application scenarios
3Reliability
If complex manual labeling processes are used, then training data quality improves, but device complexity and operational difficulty increase
Solution Approach 1:
The patent simplifies the process by copying existing image data and applying automated transformations to generate training labels. This approach replaces complex manual labeling workflows with straightforward automated processing steps, reducing operational difficulty while maintaining training data quality through controlled synthesis parameters and validation mechanisms
Data Source
AI summary
The neural network includes an encoder, a common decoder, and a residual decoder. The encoder encodes input images into a latent space. The latent space disentangles unique features from other common features. The common decoder decodes common features resident in the latent space to generate translated images which lack the unique features. The residual decoder decodes unique features resident in the latent space to generate image deltas corresponding to the unique features. The neural network combines the translated images with the image deltas to generate combined images that may include both common features and unique features. The combined images can be used to drive autoencoding. Once training is complete, the residual decoder can be modified to generate segmentation masks that indicate any regions of a given input image where a unique feature resides.