Live Sperm Morphology Segmentation for Noninvasive Subcellular Measurement
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Solution Overview
Problem
Existing methods fail to accurately measure live sperm morphology, particularly subcellular structures, due to the rapid motility of sperm cells and the need for invasive staining procedures, leading to incomplete and subjective sperm quality analysis.
Innovation Solution
A method utilizing a sperm parsing CNN for instance-aware part segmentation of sperm cells, combined with a sperm virtual staining GAN, allows for noninvasive measurement of both cellular and subcellular morphological parameters by segmenting the head, midpiece, and tail, and predicting stained images from unstained ones at different magnifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high magnification (100×) is used to detect subcellular structures, then measurement precision is improved, but the sperm cells become difficult to track due to rapid motility and limited field of view
Solution Approach 1:
The patent segments the sperm cell into distinct parts (head, midpiece, tail) and detects each part separately with specialized detectors. This allows precise measurement of subcellular structures while working with the limited field of view at high magnification, as each segment can be localized and measured independently.
Solution Approach 2:
The patent introduces a tracking system that uses low magnification footage to track sperm cell positions and provides guidance to the high magnification detection system. This intermediary tracking mechanism allows the system to maintain track of rapidly moving sperm cells while achieving high precision subcellular measurements.
2Measurement precision
If invasive staining procedures are used to visualize subcellular structures, then measurement precision is improved, but the sperm cells are killed and cannot be used for therapeutic purposes
Solution Approach 1:
The patent replaces the chemical staining mechanism with a deep learning-based image processing system. The CNN model analyzes unstained sperm images to detect and measure subcellular structures, eliminating the need for invasive chemical procedures while maintaining measurement precision and preserving sperm viability.
Solution Approach 2:
The patent creates a digital model (copy) of the sperm cell and its subcellular structures through image processing and deep learning analysis. This digital representation allows for precise measurement and evaluation without physically altering or damaging the actual sperm cell, thereby maintaining its viability for therapeutic use.
3Area of stationary object
If low magnification (20× or 40×) is used to measure kinematic parameters, then field of view is improved for tracking motility, but measurement precision of subcellular structures deteriorates
Solution Approach 1:
The patent segments the analysis into two parts: low magnification for tracking overall sperm motion and kinematic parameters, and high magnification for detailed subcellular structure measurement. The segmentation of analysis tasks across different magnification levels allows optimization of each measurement type.
Solution Approach 2:
The patent uses low magnification footage as an intermediary tracking system that provides positional information to guide high magnification detection. This intermediary system enables the transition from broad field of view tracking to focused high-precision subcellular measurement without losing track of the sperm cell.
4Device complexity
If conventional image processing methods are used to detect sperm morphology, then device complexity is reduced, but measurement precision of overlapping sperm deteriorates
Solution Approach 1:
The patent introduces a deep learning-based segmentation module as an intermediary between image acquisition and morphology measurement. This module specifically handles the complex task of separating overlapping sperm cells before passing processed information to the measurement system, thereby improving accuracy without significantly increasing overall system complexity.
Data Source
AI summary
The present invention provides a method for accurately measuring live sperm morphology, including the following steps: S11. acquiring an image including a plurality of sperm cells; S12. detecting the sperm cells in the image, and segmenting a head, a midpiece, and a tail of each detected sperm cell; and S13. calculating morphological parameters of each sperm cell based on a segmentation result. For the method for accurately measuring live sperm morphology of the present invention, an image of sperm cells is acquired, a head, a midpiece, and a tail of each sperm cell are segmented through image processing, and morphological parameters of each sperm cell are calculated based on a segmentation result. Morphological parameters of live sperm cells can be comprehensively and accurately obtained for analysis or selection.