Lipid Droplet Segmentation Using U-Net and Watershed Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional diagnosis methods for nonalcoholic fatty liver disease (NAFLD) using H&E staining are crude, time-consuming, and subjective, leading to potential errors in lipid droplet quantification.
Innovation Solution
A computer-based segmentation method using a convolutional neural network with a U-net structure and a receptive field block, combined with a watershed algorithm, to accurately segment lipid droplets in pathological slice images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional microscope observation method is used, then doctors can visually assess lipid droplets, but the process is time-consuming and subjective leading to low productivity and measurement precision
Solution Approach 1:
The patent replaces the mechanical microscope observation system with a computer-based image processing system. A convolutional neural network (U-net architecture) automatically segments and quantifies lipid droplets from H&E stained tissue images, eliminating the need for manual microscopic examination while achieving comparable or superior measurement precision and significantly reducing diagnosis time.
Solution Approach 2:
The patent creates a digital copy of the pathological tissue image and processes this copy through machine learning algorithms. The U-net model learns from annotated image copies to automatically identify and segment lipid droplets, replacing the need for doctors to physically examine each tissue sample under a microscope.
2Reliability
If conventional microscope observation method is used, then doctors can assess lipid droplet density, but the process is subjective and prone to errors
Solution Approach 1:
The patent replaces subjective human visual assessment with an objective computer-based segmentation system. The U-net convolutional neural network provides consistent, reproducible results by applying the same algorithmic criteria to each image, eliminating variability in human observation and ensuring reliable lipid droplet quantification.
Solution Approach 2:
The patent incorporates feedback mechanisms through the training process where the model learns from annotated ground truth images. The system continuously refines its segmentation capabilities based on training data, ensuring reliable and accurate lipid droplet detection that maintains consistency across different samples and observers.
3Productivity
If automated segmentation system is implemented, then productivity and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent segments the complex task of lipid droplet detection into distinct functional components: image input, U-net segmentation model, region probability map generation, boundary probability map generation, and result visualization. This modular approach manages system complexity by breaking down the automated process into manageable stages while maintaining high productivity.
Data Source
AI summary
The present invention provides a method for segmenting lipid droplets that includes: inputting a pathological slice image into a machine learning model to output a region map and an boundary map, in which the region map includes multiple regional probability values, and the boundary map includes multiple boundary probability values; and segmenting multiple lipid droplets from the pathological slice image according to the regional probability values and the boundary probability values.