Microscopy Annotation Interface for Partial Nuclei Segmentation
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Solution Overview
Problem
The challenge in digital microscopy imaging, particularly in nuclei segmentation, is the difficulty in obtaining large training data for deep learning models due to the tedious and time-consuming annotation task required by domain experts, limiting the applicability of these models when applied to different tissue types or staining protocols.
Innovation Solution
A computing system facilitates user annotation for instance segmentation using partial annotations through a user interface, encoding images to generate centroid and edge highlights, training an AI model with these encoded images, and applying morphological operations to predict and evaluate instance segmentation, enabling efficient annotation and prediction of objects of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full manual annotation of nuclei boundaries is performed by domain experts, then training data quality is improved, but annotation time and cost increase significantly
Solution Approach 1:
The patent applies partial annotation by allowing users to annotate only centroids of nuclei instead of complete boundaries. The system then uses these partial annotations to automatically generate training data with estimated boundaries through image processing algorithms, reducing annotation effort while maintaining sufficient training quality
Solution Approach 2:
The system enables self-service by automatically generating complete training annotations from partial user inputs. The annotation system processes user-provided centroids and automatically computes boundary information, allowing the system to serve itself in completing the annotation process without requiring expert manual delineation of all boundaries
2Reliability
If deep learning models are trained with extensive ground truth data, then segmentation performance is improved, but data collection complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for training by taking out centroids as the minimum required annotation. This extracted partial information is then sufficient to generate complete training examples through automatic boundary estimation, reducing data collection complexity while maintaining model training effectiveness
Solution Approach 2:
The system performs preliminary action by pre-processing user annotations to automatically generate complete training examples. Before actual model training, the system transforms partial centroid annotations into full training data structures with estimated boundaries, preparing comprehensive training data with reduced manual effort
3Loss of information
If traditional annotation methods are used for all nuclei, then complete boundary information is obtained, but user effort and time consumption increase
Solution Approach 1:
The system applies partial action by accepting only centroid annotations from users instead of requiring complete boundary delineation. This partial input is then expanded into complete training examples with estimated boundaries through automatic image processing, maintaining information completeness while reducing user effort
Solution Approach 2:
The system introduces an intermediary processing step that transforms user-provided centroid annotations into complete boundary information. This intermediary automatic boundary estimation process acts as a mediator between partial user input and the complete boundary information required for accurate segmentation training
Data Source
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AI summary
Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation via multiple regression layers, implementing instance segmentation based on partial annotations, and/or implementing user interface configured to facilitate user annotation for instance segmentation. In various embodiments, a computing system might generate a user interface configured to collect training data for predicting instance segmentation within biological samples, and might display, within a display portion of the user interface, the first image comprising a field of view of a biological sample. The computing system might receive, from a user via the user interface, first user input indicating a centroid for each of a first plurality of objects of interest and second user input indicating a border around each of the first plurality of objects of interest. The computing system might train an AI system to predict instance segmentation of objects of interest in images of biological samples.