Neural Network Feature Extraction and Mask Construction for Medical Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical image segmentation technologies face challenges in achieving accurate and efficient segmentation of medical images, particularly in terms of computational efficiency and resource utilization, especially in environments with limited medical resources.
Innovation Solution
An image processing method utilizing a feature extraction network and a mask construction network, comprising interconnected layers for feature extraction and mask generation, which employs convolutional neural networks to generate target feature and mask maps, thereby improving segmentation accuracy and speed while reducing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional medical image segmentation methods are used, then segmentation can be performed, but segmentation accuracy is insufficient and computational efficiency is low
Solution Approach 1:
The neural network is divided into a feature extraction network and a mask construction network, which operate independently but cooperatively. The feature extraction network extracts semantic features while the mask construction network generates segmentation masks, allowing parallel processing and improving both accuracy and efficiency.
Solution Approach 2:
The patent introduces multi-scale feature extraction by processing images at different resolutions and combining features from multiple layers. This dimensional approach allows the network to capture both fine details and global context, significantly improving segmentation accuracy without proportionally increasing computational cost.
2Measurement precision
If complex neural network architectures are used to improve segmentation accuracy, then accuracy improves, but memory usage increases
Solution Approach 1:
The patent extracts only the essential semantic features needed for segmentation rather than processing all image data through the entire network. By separating feature extraction from mask construction and using intermediate feature maps, memory usage is reduced while maintaining segmentation accuracy.
Solution Approach 2:
Different parts of the network are optimized for different functions: the feature extraction network uses convolutional layers optimized for feature detection, while the mask construction network uses deconvolutional layers optimized for mask generation. This localized optimization reduces overall memory requirements compared to a uniform architecture.
Data Source
AI summary
An image processing method. The method includes extracting features of an input image using a feature extraction network to generate and output a target feature map of the input image; and constructing and outputting a target mask map of the input image using a mask construction network based on the target feature map. The feature extraction network includes a feature input layer, a feature intermediate layer, and a feature output layer sequentially connected together. The mask construction network includes a mask input layer, a mask intermediate layer, and a mask output layer sequentially connected together. The feature output layer is connected to the mask input layer. The feature intermediate layer is connected to the mask intermediate layer. The feature input layer is connected to the mask output layer.