Whole-Slide Image Segmentation for Formerly Conjoined Tissue
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Solution Overview
Problem
Existing computational pathology systems struggle to accurately identify and group formerly conjoined pieces of tissue in pathology specimens, which is crucial for tasks like slide quality control, core counting, and tumor measurement, as conventional methods fail to account for natural variabilities and tears in tissue samples.
Innovation Solution
A system utilizing synthetic data generation and panoptic segmentation models to train instance segmentation systems, allowing the identification of formerly conjoined tissue pieces by introducing artificial tears and folds, and associating fragments that belong together, even across different sectioned levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional classification methods are used to identify tissue pieces, then the system is simpler and faster, but the accuracy of identifying formerly conjoined tissue pieces deteriorates
Solution Approach 1:
The patent applies instance segmentation to divide the tissue slide into distinct tissue pieces, allowing the system to identify and track individual tissue fragments that were formerly conjoined. This segmentation approach enables precise identification of tissue pieces while maintaining manageable system complexity through automated algorithms.
Solution Approach 2:
The patent uses synthetic data generation to create virtual copies of tissue images with simulated tears and folds. These synthetic training images allow the segmentation system to learn patterns of formerly conjoined tissue pieces without requiring complex real-world data, improving accuracy while keeping the system relatively simple.
2Adaptability or versatility
If synthetic data generation is used to train the segmentation system, then the ability to handle natural variabilities and tears improves, but the data preparation process becomes more complex
Solution Approach 1:
The patent performs preliminary action by pre-generating synthetic training images with simulated tears, folds, and variations before the actual tissue analysis. This advance preparation allows the segmentation system to be trained on diverse scenarios, improving its adaptability to real tissue variations without adding complexity to the actual analysis process.
Solution Approach 2:
The patent creates synthetic copies of tissue images with artificially introduced variations and defects. These virtual copies serve as training data, enabling the system to handle natural variabilities and tears in real tissue samples without requiring complex data collection and annotation processes for every possible scenario.
3Measurement precision
If instance segmentation is applied to identify tissue pieces, then the detail and localization of tissue analysis improves, but the processing time and computational resources increase
Solution Approach 1:
The patent uses pre-trained segmentation models that have learned tissue piece identification from synthetic training images. By transferring this pre-learned knowledge to real tissue analysis, the system achieves high localization accuracy without requiring time-consuming real-time training, thus reducing processing time while maintaining precision.
Solution Approach 2:
The patent performs segmentation model training and feature extraction in advance using synthetic data, so that when real tissue images are analyzed, the system can quickly apply pre-learned patterns. This preliminary action eliminates the need for complex real-time computation, reducing processing time while maintaining high localization accuracy.
Data Source
AI summary
Systems and methods are disclosed for identifying formerly conjoined pieces of tissue in a specimen, comprising receiving one or more digital images associated with a pathology specimen, identifying a plurality of pieces of tissue by applying an instance segmentation system to the one or more digital images, the instance segmentation system having been generated by processing a plurality of training images, determining, using the instance segmentation system, a prediction of whether any of the plurality of pieces of tissue were formerly conjoined, and outputting at least one instance segmentation to a digital storage device and/or display, the instance segmentation comprising an indication of whether any of the plurality of pieces of tissue were formerly conjoined.