Pathological Tissue Image Capture with ROI Weighting
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Solution Overview
Problem
Existing pathological tissue image capturing systems fail to automatically detect regions of interest (ROI) and select appropriate enlarged image capturing ranges, leading to inefficiencies in pathological tissue diagnosis, as they rely on manual operation and do not accurately prioritize regions based on tissue density or cell nuclei presence.
Innovation Solution
A system comprising a pathological image acquirer, a weighting device to detect and weight pixels in ROI, and a range selector to automatically select an enlarged image capturing range based on pixel weights, allowing for automated detection and capture of enlarged pathological tissue images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual operation is used to select regions for enlarged image capture, then the pathologist can identify regions of interest based on experience, but the process is time-consuming and lacks automation
Solution Approach 1:
The system enables self-service by allowing the pathological tissue image capturing system to automatically detect regions of interest and select enlarged image capturing ranges without requiring manual operation by the pathologist. The weighting device automatically analyzes tissue density and cell nuclei presence to identify significant regions, and the range selector automatically determines the optimal capturing range based on these weighted pixels.
2Measurement precision
If uniform weighting is applied to all pixels, then the processing is simple, but regions of interest cannot be distinguished from background tissue
Solution Approach 1:
The weighting device applies local quality by assigning different weights to different pixels based on their local characteristics. Pixels corresponding to regions with high tissue density or numerous cell nuclei are assigned higher weights, while pixels in background or less significant regions receive lower weights. This differential weighting enables precise identification of regions of interest without requiring complex external intervention.
3Loss of information
If enlarged images are captured from all regions, then comprehensive diagnostic information is obtained, but the data volume and processing time increase significantly
Solution Approach 1:
The system extracts only the essential information by selectively capturing enlarged images from regions of interest identified through the weighting mechanism. Instead of capturing and processing images from all regions, the range selector extracts and focuses on pixels with higher weights that correspond to diagnostically significant areas, thereby maintaining diagnostic completeness while improving efficiency.
4Adaptability or versatility
If the capturing range is fixed, then the system is simple to operate, but it cannot adapt to different tissue densities and cell distributions
Solution Approach 1:
The range selector implements dynamics by adaptively determining the enlarged image capturing range based on the weighted pixel distribution. Rather than using a fixed capturing range, the system dynamically adjusts the capturing range to encompass regions with higher pixel weights, which correspond to areas with higher tissue density or cell nuclei presence. This dynamic adaptation allows the system to effectively handle various tissue types and diagnostic scenarios.
Data Source
AI summary
A pathological tissue image capturing system includes a pathological image acquirer 100 for capturing a pathological tissue image and an output device 120 for outputting the pathological tissue image. The pathological tissue image capturing system also includes a weighting device 111 for detecting a ROI from the pathological tissue image and adding a weight to pixels positioned in the ROI, and a range selector 112 for selecting an enlarged image capturing range in which to capture the pathological tissue image at an enlarged scale based on the weight added by the weighting device 111. The pathological image acquirer 100 captures an enlarged pathological tissue image in the enlarged image capturing range selected by the range selector 112. The output device 120 outputs the captured enlarged pathological tissue image in the enlarged image capturing range.