Medical Image Serial Section Detection via Segment Clustering
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Solution Overview
Problem
Current methods for analyzing pathology slide images often misidentify serial sections of the same tissue, leading to inaccurate diagnoses and potential misdiagnosis due to recognizing sections as different tissues.
Innovation Solution
A method using a computing device to detect segments in medical images, estimate the number of tissue sections and the distance between them, and identify serial sections by employing deep learning models and geometric transformations to accurately group segments as part of the same tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple tissue sections are analyzed as different tissues, then each section can be independently examined, but diagnostic accuracy deteriorates due to misidentification of serial sections
Solution Approach 1:
The method segments the pathology slide image into multiple candidate segments and then clusters them based on feature similarity. This segmentation approach allows independent analysis of each section while maintaining the ability to identify serial sections as belonging to the same tissue, thereby resolving the contradiction between independent examination and accurate identification.
Solution Approach 2:
The invention changes parameters by extracting multiple types of features (color, texture, shape) from tissue sections and using these transformed parameters for clustering. This parameter transformation enables the system to distinguish between truly different tissues and serial sections of the same tissue, improving both diagnostic accuracy and identification reliability.
2Measurement precision
If deep learning models and geometric transformations are used to identify serial sections, then identification accuracy improves, but computational complexity increases
Solution Approach 1:
The method performs preliminary actions by pre-processing the pathology slide image to extract candidate segments and their features before clustering. Geometric transformations and feature extractions are performed in advance, which simplifies the subsequent clustering process and makes the overall system more manageable despite the complexity of individual steps.
Solution Approach 2:
The invention introduces intermediary elements such as feature vectors and distance metrics that mediate between the raw image data and the final clustering results. These intermediaries transform complex image data into manageable numerical representations, reducing computational complexity while maintaining identification accuracy.
Data Source
AI summary
Disclosed is a method for detecting a serial section of a medical image, which is performed by a computing device. The method may include: detecting segments included in at least one tissue which exists in the medical image; estimating a number of tissue sections corresponding to the serial section and a distance between the segments based on the segments; and distinguishing tissue sections corresponding to the serial section based on the estimated number of tissue sections corresponding to a serial section and the distance between the segments.