Cell Image Segmentation Using Scribble Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cell segmentation methods face challenges in efficiently segmenting clustered cells due to the high cost and labor intensity of generating ground-truth labels, and the sensitivity of deep learning-based methods to point annotations, which require extensive computational resources and are prone to errors.
Innovation Solution
A cell image segmentation method using scribble labels that iteratively pre-trains via an image segmentation network (U-Net) with scribble labels, calculates exponential moving averages of prediction probabilities, and self-trains by assigning pseudo-labels to refine segmentation probabilities using scribbled and unscribbled losses, reducing the need for additional image processing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground-truth labels are used for training deep neural networks, then segmentation accuracy is improved, but dataset building time and cost increase significantly
Solution Approach 1:
The model performs self-training by generating its own pseudo-labels from scribble annotations through iterative refinement. The system uses its own predictions to create training labels, eliminating the need for extensive manual ground-truth label creation while maintaining segmentation accuracy
Solution Approach 2:
The method performs preliminary training with scribble labels to generate initial pseudo-labels, which are then refined iteratively. This preliminary action with minimal annotations prepares the model for accurate segmentation without requiring complete ground-truth labels upfront
2Ease of manufacture
If point annotations are used for cell segmentation, then dataset building effort is reduced, but segmentation performance becomes highly sensitive to point location
Solution Approach 1:
Instead of requiring precise point annotations at critical locations, the method uses scribble annotations that cover partial regions of cells. This partial action approach is more forgiving of annotation imprecision while still enabling accurate segmentation through iterative pseudo-label refinement
3Ease of manufacture
If existing scribble-supervised learning methods are used, then manual label generation burden is reduced, but computational costs increase due to pre-processing and post-processing procedures
Solution Approach 1:
The method extracts and removes the computationally expensive pre-processing and post-processing steps (Graph-Cut, DenseCRF) from the existing scribble-supervised learning pipeline. By directly optimizing the segmentation network with scribble labels and pseudo-labels, it eliminates unnecessary computational overhead while maintaining the benefit of reduced annotation burden
Data Source
AI summary
A cell image segmentation method using scribble labels includes iteratively pre-training via an image segmentation network (U-Net) using a cell image and scribble labels indicating a cell region and a background region as training data, calculating an exponential moving average (EMA) of image segmentation prediction probabilities at a predetermined interval during the pre-training, self-training by assigning the cell region and the background region for which the EMA of image segmentation prediction probabilities is over a preset threshold to be a pseudo-label, and iteratively refining the image segmentation prediction probability based on a scribbled loss (Lsp) obtained through a result of the training and an unscribbled loss (Lup). Accordingly, it is possible to achieve cell image segmentation with high reliability using only scribble labels.