Microscopic Image Motion Analysis for Overlapping Nematodes
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Solution Overview
Problem
Conventional microscopic image analysis of nematodes is labor-intensive and inefficient, with high labor costs, low accuracy, and inability to handle complex scenarios such as overlapping or twisting nematodes, requiring manual intervention and prior knowledge for segmentation and skeleton extraction.
Innovation Solution
A deep learning-based microscopic image analysis framework that includes instance segmentation, skeleton extraction, and principal component analysis, automating processes like counting, segmentation, and kinematic analysis, using models like bilayer instance segmentation and SVM for head-tail recognition, reducing manual operations and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based image processing is used to improve diagnostic accuracy, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent segments the image processing workflow into distinct stages: preprocessing (noise filtering, contrast enhancement), feature extraction (identifying cellular structures, nuclei, cytoplasm), and deep learning-based classification. This segmentation allows critical path operations to be optimized separately, enabling parallel processing of independent stages and reducing overall processing time while maintaining diagnostic accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing images before deep learning analysis, including noise reduction, contrast normalization, and preliminary segmentation. These preliminary steps reduce the complexity of the main deep learning task, allowing faster processing while preserving the diagnostic information needed for accurate classification.
2Measurement precision
If multiple images are captured to improve measurement precision, then diagnostic accuracy is improved, but the time required for observation increases
Solution Approach 1:
The patent merges multiple image capture operations into a single coordinated imaging sequence, where multiple focal planes or angular views are captured in rapid succession without requiring repeated sample preparation or re-positioning. This combining approach maintains the ability to perform multi-image analysis for improved precision while minimizing the total observation time through efficient resource utilization.
3Productivity
If conventional image processing methods are used, then processing speed is maintained, but measurement precision and diagnostic accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between conventional image processing and final diagnostic interpretation. This intermediary consists of deep learning models that process extracted features and provide probabilistic classifications, allowing conventional fast processing methods to continue operating while enhancing diagnostic accuracy through the intermediary's pattern recognition capabilities.
4Measurement precision
If advanced image processing techniques are applied to improve diagnostic accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal deep learning framework that can process multiple types of microscopic images (different stains, different magnifications, different sample types) through a single trained model architecture. This multi-functionality reduces device complexity by eliminating the need for separate processing pipelines for different image types, while still achieving high diagnostic accuracy across diverse applications.
Data Source
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AI summary
The present application discloses a microscopic image processing method and apparatus, a computer device, and a storage medium. The method comprises: performing instance segmentation on a microscopic image to obtain an instance image; performing skeleton extraction on a target object in the instance image to obtain skeleton form information of the target object; performing motion analysis on the target object on the basis of the skeleton form information to obtain a plurality of feature values; and determining a feature value sequence composed of the plurality of feature values as motion component information of the target object.