Stained Sample Cell Cluster Detection for Ground Truth Annotation
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Solution Overview
Problem
The challenge in digital pathology is the laborious process of obtaining ground truth annotations for cell and/or nucleus detection in images of varying sizes, resolutions, and stain types, particularly due to the difficulty in applying models tuned for specific resolutions or stains to datasets with different characteristics.
Innovation Solution
A system is developed to generate homogeneous clusters of cells and/or nuclei using a pre-trained convolutional neural network, which is adapted from models like Fast-RCNN or Faster-RCNN, and further utilizes agglomerative hierarchical clustering to reduce the burden of ground truth labeling by transforming the system input to annotated clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground truth annotations are obtained by manually labeling individual cells, then annotation accuracy is improved, but annotation time and labor cost increase significantly
Solution Approach 1:
The patent merges multiple individual cell annotations into cluster-level annotations. Instead of labeling each cell separately, the system identifies clusters of cells and annotates them as single units, thereby reducing annotation time while maintaining accuracy through the cluster representation
Solution Approach 2:
The patent segments the annotation task into two levels: cluster identification and cell detection within clusters. The complex task of annotating every cell is divided into annotating clusters first, then using automated methods to detect individual cells within those clusters, reducing overall annotation burden
2Measurement precision
If a model is tuned for a specific resolution or stain type, then detection accuracy for that specific case is improved, but adaptability to different resolutions or stains deteriorates
Solution Approach 1:
The patent develops a universal cell detection model that can handle multiple stain types (H&E, IHC, ISH) and various resolutions without requiring separate tuned models for each case. The model is designed to be multi-functional, adapting to different input characteristics while maintaining detection accuracy
Solution Approach 2:
The patent employs parameter adjustment mechanisms that allow the model to adapt to different stain types and resolutions by changing its internal parameters dynamically, rather than requiring complete re-tuning for each new condition
3Loss of information
If individual cells are annotated separately, then cell-level detail is preserved, but annotation burden increases from order of number of cells to manageable level
Solution Approach 1:
The patent combines multiple cell annotations into cluster annotations, reducing the number of annotation units from individual cells to clusters. This merging approach maintains cell-level information through automated detection within clusters while making the annotation process manageable
Data Source
AI summary
The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.