User-Guided Domain Adaptation for Medical Image Segmentation
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Solution Overview
Problem
Current methods for medical image annotation, particularly for 3D data, face challenges in effectively leveraging user interactions to guide mask generation, with deep-learning approaches struggling to harmonize mask predictions with user inputs, leading to labor-intensive annotation processes.
Innovation Solution
A user-guided domain adaptation (UGDA) method that combines two fully convolutional networks (FCNs) to generate extreme-point and mask predictions, using adversarial loss to align predictions with user interactions, allowing for minimal-labor annotation and learning from both labeled and unlabeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep-learning approaches are used to generate mask predictions, then segmentation performance is improved, but alignment with user interactions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where user interactions (extreme points, scribbles, boundary marks) are continuously incorporated to guide and correct mask predictions. The system uses the predicted mask to generate synthetic user interactions, compares them with actual user inputs, and iteratively refines the segmentation to improve alignment while maintaining deep-learning performance
Solution Approach 2:
The patent introduces an intermediary component that acts as a bridge between deep-learning mask predictions and user interactions. This intermediary generates synthetic user interactions from predicted masks and uses them to train a discriminator, which then guides the segmentation model to produce masks that better align with actual user inputs without sacrificing segmentation accuracy
2Measurement precision
If full annotation of 3D volumes is performed, then segmentation accuracy is improved, but labor requirement increases
Solution Approach 1:
The patent applies partial action by using only minimal user interactions (a few extreme points, scribbles, or boundary marks) instead of full 3D volume annotation. The deep-learning model performs the bulk of the segmentation work, while user inputs provide guiding constraints, dramatically reducing labor while maintaining or improving accuracy through the adversarial training framework
Solution Approach 2:
The system enables self-service annotation where the model generates its own synthetic training data from predicted masks and user interactions. The adversarial training framework allows the model to automatically refine its predictions by learning from the discrepancy between synthetic and actual user interactions, reducing dependence on extensive manual annotation
3Manufacturing precision
If user interactions are used to guide mask generation, then annotation quality is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple components into an integrated adversarial training framework that combines mask prediction, synthetic interaction generation, discriminator training, and guidance implementation. This unified approach manages complexity by coordinating all components through a single training loop that minimizes the adversarial loss function, rather than treating each component as a separate system
Data Source
AI summary
The present disclosure provides a computer-implemented method, a device, and a computer program product using a user-guided domain adaptation (UGDA) architecture. The method includes training a combined model using a source image dataset by minimizing a supervised loss of the combined model to obtain first sharing weights for a first FCN and second sharing weights for a second FCN; training a discriminator by inputting extreme-point/mask prediction pairs for each of the source image dataset and a target image dataset and by minimizing a discriminator loss to obtain discriminator weights; and finetuning the combined model by predicting extreme-point/mask prediction pairs for the target image dataset to fool the discriminator by matching a distribution of the extreme-point/mask prediction pairs for the target image dataset with a distribution of the extreme-point/mask prediction pairs for the source image dataset.