Eukaryotic Cell Image Augmentation With Accurate Instance Masks
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Solution Overview
Problem
The generation of large, well-varied training datasets for eukaryotic cell image analysis is time-consuming and labor-intensive, and existing data augmentation methods, such as Generative Adversarial Networks (GANs), are unsuitable for generating accurate instance segmentation masks, posing risks in medical applications due to the potential for uncontrolled, noisy, and unreliable images.
Innovation Solution
A method involving the extraction of single-cells and multi-cells from images, application of augmentation techniques, and generation of artificial images with correct instance segmentation masks using autoencoders and Greedy AutoAugment to maintain authenticity, while creating background images that mimic the original distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually annotated microscopical datasets are created for training ANNs, then the quality and accuracy of training data improve, but the time and labor required increase significantly
Solution Approach 1:
The patent creates synthetic training images by copying and transforming real cell images through various augmentation operations (rotation, flipping, cropping, color jittering) to generate large datasets without manual annotation. This allows rapid generation of training data while maintaining quality through controlled transformations that preserve biological realism.
Solution Approach 2:
The system performs preliminary extraction and segmentation of cells from source images before augmentation, creating a library of pre-processed cell instances that can be rapidly combined and transformed. This preliminary preparation enables fast dataset generation while maintaining annotation quality, as the segmentation is done once rather than repeatedly for each augmented image.
2Quantity of substance
If existing GAN-based augmentation methods are used, then the quantity of training data increases, but the reliability and accuracy of generated images deteriorate due to noise and lack of instance segmentation masks
Solution Approach 1:
The patent segments cells into individual instances with precise masks before augmentation, ensuring that each generated image contains accurately defined cell boundaries. This segmentation approach maintains reliability by preserving instance-level information throughout the augmentation process, unlike GANs that generate pixels without structural understanding.
Solution Approach 2:
The system uses an intermediary representation of segmented cell masks as a bridge between real images and augmented images. These masks serve as a reliable intermediary structure that guides the generation process, ensuring that synthesized images maintain accurate cell boundaries and instance segmentation information throughout augmentation.
3Reliability
If large manually annotated datasets are created, then the performance of ANNs improves, but the productivity of research decreases due to the lengthy annotation process
Solution Approach 1:
The patent copies existing annotated cell images and applies transformations to create synthetic training datasets, eliminating the need for time-consuming manual annotation while maintaining the quality needed for ANN training. This copying approach enables rapid dataset generation that keeps pace with research productivity requirements.
Solution Approach 2:
The system performs self-service annotation by automatically extracting, segmenting, and augmenting cell images without human intervention. This automated self-service capability generates large training datasets rapidly, maintaining high ANN performance while eliminating the productivity bottleneck of manual annotation.
Data Source
AI summary
A method of generating artificial images for medical evaluation of eukaryotic cells can include extracting each instance of single-cells in an image of eukaryotic cells; extracting each instance of multi-cells in the image of eukaryotic cells; generating a background image from the image of eukaryotic cells; selecting a set of cells from the extracted single-cells and the extracted multi-cells; applying at least one augmentation technique to each cell in the set of cells to generate augmented cells; and generating an artificial image of eukaryotic cells using the augmented cells and the background image.