Image Segmentation Quality Prediction for Digital Pathology
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Solution Overview
Problem
Existing methods for nucleus/cell segmentation and detection in digital pathology imaging fail to accurately segment nuclei and other micro-anatomic structures due to heterogeneity in tissue specimens, and existing quality control systems are inefficient in detecting and correcting errors in segmentation algorithms, especially in high-resolution images with millions of nuclei.
Innovation Solution
A machine-learning-based semi-automated quality assessment system and method that uses patch-level intensity and texture features to predict segmentation quality, which includes training a classification model to evaluate and adjust segmentation parameters, using a system and method that predicts segmentation quality of objects in images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality assessment is performed on large image datasets, then segmentation quality can be evaluated, but the process becomes extremely labor intensive and inefficient
Solution Approach 1:
The system enables automated self-assessment of segmentation quality by training a classification model to evaluate segmentation results without human intervention. The model uses features extracted from segmented objects and image patches to predict segmentation quality, allowing the system to autonomously identify and flag poor-quality segmentations for review.
Solution Approach 2:
The patent replaces manual mechanical inspection by pathologists with an automated machine learning-based classification system. The system extracts features from image data and segmented objects, applies a trained classification model to predict segmentation quality, and automatically identifies regions requiring human review, substituting the mechanical process of manual examination with computational analysis.
2Manufacturing precision
If segmentation algorithms are optimized for specific images, then segmentation quality improves for those images, but the parameters do not perform well across multiple images with tissue heterogeneity
Solution Approach 1:
The system applies local quality assessment by evaluating segmentation results at the patch level rather than globally across entire images. It extracts features from individual image patches and segmented objects to predict segmentation quality locally, allowing different regions with varying tissue characteristics to be assessed independently. This enables the system to identify locally poor-quality segmentations without requiring global parameter optimization.
Solution Approach 2:
The patent implements parameter changes by using a classification model that predicts segmentation quality based on extracted features, enabling dynamic identification of regions requiring parameter adjustment. The system can flag areas where segmentation parameters should be modified to improve quality, allowing adaptive optimization without retraining segmentation algorithms on each new image set.
3Reliability
If a robust error assessment stage is implemented, then segmentation quality control is improved, but the system complexity increases
Solution Approach 1:
The system segments the quality assessment process into distinct stages: feature extraction from image patches and segmented objects, classification model prediction of segmentation quality, and identification of poor-quality regions. This segmented approach to quality control breaks down the complex assessment task into manageable computational steps, improving reliability without proportionally increasing overall system complexity.
Solution Approach 2:
The classification model serves as an intermediary between the segmentation algorithm and final quality determination. It takes features from segmented objects and image patches as input and outputs predicted segmentation quality, acting as a mediator that simplifies the quality assessment process while maintaining robust error detection capabilities across diverse tissue types.
Data Source
AI summary
A method of testing an impedance-sensitive system with a switching device, wherein the method comprises: switching the disconnecting device into the on-state configured to permit transmission of energy via the coil; implementing a first measurement with the impedance-sensitive system; switching the disconnecting device into the off-state configured to permit damping of the external positioning signal that couples into the coil so as to reduce the undesirable oscillations of the coil; implementing a second measurement with the impedance-sensitive system; performing a comparison of the first measurement and the second measurement; performing a verification of the comparison with a target specification; and displaying a correct function and/or a malfunction depending on the verification.