Convolutional Neural Network for Cell Segmentation and Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning methods for cell classification in biological images rely heavily on shape features and single image patches, leading to inaccurate cell segmentation and classification, especially when cell centers are not accurately determined due to segmentation errors, and fail to capture multi-level features and discriminative appearance factors.
Innovation Solution
A deep convolutional neural network with multiple convolutional layers using kernels of different sizes for feature extraction, a dual branch network architecture for cell segmentation and classification, where the first branch generates a cell segmentation map and the second branch performs classification using feature maps from multiple shifted patches to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning methods using shape features and single image patches are used, then the system is simple and fast, but cell segmentation and classification accuracy deteriorates, especially when cell centers are not accurately determined
Solution Approach 1:
The patent applies segmentation by dividing the classification task into multiple branches: a first branch for segmentation and a second branch for classification. The second branch further divides classification into multiple classifiers that process different shifted patches. This segmentation allows each component to specialize, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimension by processing multiple shifted patches (different spatial positions and scales) for each cell instance. Instead of relying on a single image patch, the system extracts features from multiple patches at different locations and scales, then aggregates them. This dimensional expansion in the feature space compensates for inaccurate cell center determination and improves classification robustness.
2Reliability
If deep convolutional neural networks with multiple convolutional layers are used, then feature extraction capability improves, but computational complexity and training time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the deep convolutional neural network on a large dataset (e.g., ImageNet) to learn general feature representations before fine-tuning on the specific cell classification task. This pre-training phase captures generic visual features, and subsequent fine-tuning adapts them to cell-specific patterns, reducing the training time required for the specialized task while maintaining high feature extraction accuracy.
Solution Approach 2:
The patent merges the segmentation and classification tasks into a unified deep learning framework where both tasks share the same feature extraction backbone. The convolutional layers extract features that are simultaneously useful for both segmentation and classification, avoiding redundant computation. The shared weights and joint training approach reduce overall training time compared to training separate models independently.
3Measurement precision
If multiple classifiers process different shifted patches for classification, then classification accuracy improves by handling segmentation errors, but computational load increases
Solution Approach 1:
The patent applies partial action by having multiple classifiers process only shifted patches relevant to each cell instance rather than the entire image. Each classifier focuses on a specific local region (shifted patch), and the results are aggregated. This partial processing approach improves accuracy by covering potential segmentation errors while reducing computational load compared to processing the whole image through multiple independent classifiers.
Data Source
AI summary
An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.