Medical Image Segmentation Using Pre-trained Decoder Shape Priors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image segmentation technologies face challenges such as domain mismatch and image quality variations, leading to issues like over-segmentation and under-segmentation, particularly in medical imaging, due to practical constraints and complexities.
Innovation Solution
A neural network-based system comprising an encoder and a decoder network, where the decoder network is pre-trained to learn a shape prior associated with anatomical structures, and is used to constrain the output of the encoder network during training, employing iterative training methods and co-training with another encoder network to improve segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used, then processing speed is maintained, but segmentation accuracy deteriorates due to domain mismatch and image quality variations
Solution Approach 1:
The decoder network is pre-trained on a large dataset of anatomical structures to learn shape priors and anatomical patterns before being deployed. This preliminary training enables the network to adapt to different domains and image qualities without requiring extensive retraining, thereby improving segmentation accuracy across varying conditions while maintaining domain adaptability
Solution Approach 2:
The system uses iterative training to progressively refine the encoder network's parameters. By changing parameters incrementally through multiple training iterations with the pre-trained decoder as a constraint, the system achieves better segmentation accuracy while adapting to different domains and image variations
2Measurement precision
If the decoder network is pre-trained to learn shape prior, then segmentation accuracy is improved, but training time and computational complexity increase
Solution Approach 1:
The decoder network undergoes pre-training on a large dataset to learn shape priors and anatomical patterns before being deployed. This preliminary training enables the network to adapt to different domains and image qualities without requiring extensive retraining, thereby improving segmentation accuracy across varying conditions while maintaining domain adaptability
Solution Approach 2:
The system employs iterative training where the encoder and decoder networks are continuously refined together. The pre-trained decoder serves as a persistent constraint throughout the training process, guiding the encoder's parameter optimization continuously rather than through discrete, time-consuming retraining cycles
3Manufacturing precision
If iterative training with pre-trained decoder constraint is used, then over-segmentation and under-segmentation are reduced, but device complexity increases
Solution Approach 1:
The encoder and decoder networks are trained together in an integrated iterative process, merging their training cycles. The pre-trained decoder serves as a constraint that guides the encoder's learning, combining shape prior knowledge with image-specific features in a unified training framework that reduces over-segmentation and under-segmentation while managing architectural complexity
Data Source
AI summary
Described herein are neural network-based systems, methods and instrumentalities associated with image segmentation that may be implementing using an encoder neural network and a decoder neural network. The encoder network may be configured to receive a medical image comprising a visual representation of an anatomical structure and generate a latent representation of the medical image indicating a plurality of features of the medical image. The latent representation may be used by the decoder network to generate a mask for segmenting the anatomical structure from the medical image. The decoder network may be pre-trained to learn a shape prior associated with the anatomical structure and once trained, the decoder network may be used to constrain an output of the encoder network during training of the encoder network.