Image Segmentation Model Using Pre-trained Tissue Distribution
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Solution Overview
Problem
Existing image segmentation methods require technicians to analyze images and design models for each specific human tissue, leading to poor versatility and applicability, as they cannot be directly applied to new images without redesigning models.
Innovation Solution
An image segmentation method that pre-trains an initial model on multiple human tissue images to obtain distribution information, which is then trained further to create an image segmentation model that can segment target human tissues without requiring manual analysis, using a combination of 3D and 2D image segmentation modules for flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technicians manually analyze images and design models for each specific human tissue, then segmentation accuracy for that specific tissue is improved, but versatility and applicability deteriorate
Solution Approach 1:
The patent creates a universal image segmentation model that can handle multiple types of human tissues through a unified framework. The model integrates both 3D and 2D segmentation modules that can process different tissue types (abdominal tissues, natural images, and other human tissues) without requiring separate manual model design for each tissue type, thus achieving multi-functionality while maintaining segmentation accuracy.
Solution Approach 2:
The patent performs preliminary training of the image segmentation model using a large dataset of first sample images containing multiple human tissues before actual segmentation tasks. This pre-training establishes a foundational understanding of various tissue characteristics, enabling the model to adapt to new tissue types without requiring technicians to manually analyze and redesign models for each specific tissue.
2Reliability
If separate models are designed for different human tissues, then specialization for each tissue type is improved, but device complexity and time consumption deteriorate
Solution Approach 1:
The patent merges 3D and 2D segmentation modules into a single unified image segmentation model. The model can dynamically select and combine appropriate segmentation approaches (3D for abdominal tissues, 2D for natural images and other human tissues) within one framework, eliminating the need for multiple separate specialized models and reducing overall system complexity.
Solution Approach 2:
The patent implements a dynamic model selection mechanism where the system automatically determines whether to apply 3D or 2D segmentation based on the input image type and requirements. This dynamic adaptability allows the single model to specialize in different tissue types as needed without requiring static pre-configured separate models for each tissue type.
3Adaptability or versatility
If manual image analysis and model design is performed for each new image, then adaptability to specific requirements is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The patent enables the image segmentation model to automatically adapt to new human tissue images through self-learning mechanisms. The model can process images of target human tissues without requiring technicians to manually analyze each new image and redesign models, as it autonomously adjusts its parameters and structures based on the input data, significantly improving segmentation efficiency while maintaining adaptability.
Solution Approach 2:
The patent allows the model parameters and structures to change dynamically based on the input image characteristics. Through parameter adjustment and optimization during the segmentation process, the model adapts to different tissue types and requirements automatically, eliminating the need for manual model redesign while maintaining high adaptability to specific segmentation needs.
Data Source
AI summary
An image segmentation method is provided for an electronic device. The method includes pre-training a first initial model based on a plurality of first sample images to obtain a second initial model, the plurality of first sample images including images of a plurality of human tissues, and the second initial model including distribution information of a plurality of target regions corresponding to the plurality of human tissues, training the second initial model based on a plurality of second sample images to obtain an image segmentation model, image information of the plurality of second sample images including at least distribution information of a plurality of target regions corresponding to a target human tissue reflected in the second sample images, and feeding a first image to the image segmentation model, and segmenting, by using the image segmentation model, the first image according to the image information to output a second image.