Cell Body Segmentation Using ML Nuclei Seeding and Watershed
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Solution Overview
Problem
Current methods for detecting cell nuclei and cell membranes in cell transcriptomics analysis are manual, time-consuming, and require tuning multiple parameters for specific use cases, limiting their applicability.
Innovation Solution
A deep cell body segmentation method using a trained machine learning model to identify cell nuclei and automatically detect cell membranes through a watershed technique, utilizing a single parameter for various biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to detect cell nuclei and membranes, then measurement precision can be achieved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical detection methods with an automated machine learning system. A trained deep neural network model processes images to automatically identify cell nuclei and membranes, eliminating the need for manual intervention while maintaining high measurement precision and significantly reducing time consumption.
Solution Approach 2:
The system performs self-service by automatically detecting and segmenting cell structures without requiring user input. The machine learning model independently processes images, identifies nuclei and membranes, and generates segmentation masks, making the system autonomous and efficient.
2Measurement precision
If multiple parameters are tuned for specific use cases, then measurement precision improves, but device complexity increases and adaptability decreases
Solution Approach 1:
The patent creates a universal machine learning model that can detect cell nuclei and membranes across different biological samples without requiring re-tuning for each specific use case. The model is trained on diverse data and maintains high accuracy across various cell types and staining protocols, eliminating the need for parameter optimization for each application.
Solution Approach 2:
The system handles parameter variations implicitly through its training process. Instead of requiring manual tuning of multiple parameters for different use cases, the model learns to adapt to varying conditions during inference, maintaining consistent performance across different biological samples and staining methods.
3Measurement precision
If separate systems are used for nuclei and membrane detection, then measurement precision for each function is optimized, but device complexity increases and ease of operation decreases
Solution Approach 1:
The patent merges nuclei detection and membrane detection into a single integrated machine learning system. The unified model processes images once to simultaneously identify both nuclear and membrane structures, eliminating the need to operate separate systems and simplifying the workflow while maintaining high precision for both functions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and accurate segmentation of cell nuclei and membranes across different cell lines with high image resolution, reducing manual input and improving gene information mapping in biological samples.
Implementation Method 1
processing the first image with a trained machine learned model, wherein the trained machine learning model outputs locations of a plurality of cell nuclei in the first stained image
Implementation Method 2
processing the second image using the plurality of seed points to determine a plurality of cell membranes
Data Source
AI summary
A system and method of performing deep cell body segmentation on a biological sample is provided. The method includes receiving a first and a second stained image. The first image is processed using a trained machine learned model that outputs locations of a plurality of cell nuclei in the first stained image. Seed points are then determined based on the locations of the plurality of cell nuclei. The second image is then processed using the seed points to determine a plurality of cell membranes using a watershed segmentation. The second image is then post-processed and an output image is produced. The output image is then analyzed and gene sequencing is performed.