Bottom-up Instance Segmentation for Overlapping Nuclei
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Solution Overview
Problem
Current nuclear segmentation methods face challenges in accurately segmenting cells or nuclei with translucent stacks and occlusion due to limitations in maintaining morphological features and handling overlapping instances, especially in histopathology images, where conventional top-down methods are inefficient and manual delineation is prone to variability.
Innovation Solution
A bottom-up instance segmentation method using spatial embedding to identify instance boundaries and shared pixels, employing a flexible Gaussian margin and unique matching algorithms to construct complete instance masks, thereby adapting to various sizes and patterns, and avoiding excessive penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If top-down segmentation method is used, then segmentation performance on modal data is impressive, but inference time is slow for data with large number of instances
Solution Approach 1:
The patent inverts the conventional top-down segmentation approach by implementing a bottom-up segmentation method. Instead of detecting objects first and then segmenting them, the method segments pixels first and then groups them into instances. This inversion enables parallel processing of pixel assignments, significantly reducing inference time while maintaining segmentation accuracy through the use of spatial embedding and clustering algorithms.
Solution Approach 2:
The patent applies segmentation at the pixel level by assigning each pixel to a unique predictive instance through spatial embedding. This fine-grained segmentation approach allows for efficient parallel computation and enables the bottom-up method to handle large numbers of instances quickly, resolving the time-performance tradeoff.
2Measurement precision
If conventional modal segmentation method is used, then nuclei detection is performed, but exact delineation and separation of instances in overlapping clusters is problematic
Solution Approach 1:
The patent introduces spatial embedding as an intermediary representation that captures the spatial relationships between pixels and instances. This spatial embedding serves as a mediator that enables accurate delineation of instance boundaries in overlapping clusters by providing a continuous spatial framework for pixel assignment, resolving the delineation problems of conventional modal segmentation.
Solution Approach 2:
The patent changes the parameter representation by using spatial embedding coordinates and clustering bandwidth parameters instead of direct pixel classification. This parameter transformation allows for more precise control over instance boundaries and enables accurate separation of overlapping instances through optimized clustering parameters.
3Productivity
If bottom-up method with single center spatial embedding is used, then inference speed is improved, but instances cannot share pixels due to center target loss
Solution Approach 1:
The patent extends the spatial embedding approach to support multiple attraction centers per instance, making the method universally applicable to both simple and complex nuclear structures. This multi-center capability allows instances to share pixels in overlapping regions while maintaining the computational efficiency of the bottom-up approach, achieving both speed and adaptability.
Solution Approach 2:
The patent introduces dynamic clustering bandwidth parameters that can adapt to different instance sizes and densities. This dynamic adjustment enables the system to flexibly handle varying nuclear morphologies and overlapping patterns, allowing pixel sharing where needed while maintaining inference speed through efficient parameter optimization.
4Manufacturing precision
If manual delineation is used, then detailed structured annotation is obtained, but variability between observers reduces reproducibility
Solution Approach 1:
The patent implements self-service annotation by training the model to automatically learn and reproduce annotation standards from training data. The system performs self-consistent segmentation without human intervention, eliminating observer variability while maintaining detailed annotation quality through supervised learning from expert-labeled training datasets.
Data Source
AI summary
Disclosed are a bottom-up instance segmentation method and apparatus. The bottom-up instance segmentation method includes acquiring an image, identifying a boundary of each instance and a shared pixel between instances by encoding the image into a seed map and a plurality of sigma maps based on a previously trained bottom-up segmentation model, and outputting a segmented image for an object in the image based on the boundary of each instance and the shared pixel between instances.