Automated CTC Detection via Cell Feature Thresholding
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Solution Overview
Problem
Conventional systems for detecting circulating tumor cells (CTCs) are inefficient due to the need for manual processing, resulting in low throughput and slow data processing, making it difficult to quickly identify CTCs in blood samples where they are outnumbered by other cells.
Innovation Solution
An automated method and system that uses cell feature-based analysis, including CD45 and DAPI staining protocols, to detect CTCs by processing low-resolution images, determining threshold values, performing a gating process, and verifying potential CTCs through user confirmation, with the option to acquire and verify high-resolution images for confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing is used for CTC detection, then detection accuracy can be maintained through expert judgment, but throughput is low and data processing is slow
Solution Approach 1:
The system enables automated self-detection of CTCs through computer-based image processing and analysis. The automated system performs threshold determination, cell identification, and gating processes without requiring continuous manual intervention, thereby increasing throughput while maintaining detection capability.
Solution Approach 2:
The patent replaces manual mechanical processing with computer-based automated processing. Image analysis, threshold determination, and cell identification are performed algorithmically rather than manually, substituting human-operated mechanical processes with automated computational systems to improve throughput.
2Loss of time
If automated processing is implemented, then throughput and data processing speed increase, but system complexity increases
Solution Approach 1:
The automated detection process is divided into distinct sequential stages: image acquisition, threshold determination, potential cell identification, gating process, and verification. This segmentation allows each stage to be optimized independently and processed efficiently, reducing overall data processing time while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary automated processing steps including threshold determination and potential cell identification before final verification. This preliminary action filters out obvious non-CTC events early in the process, reducing the burden on subsequent verification steps and accelerating overall data processing.
3Productivity
If automated detection is used, then high throughput is achieved, but verification and confirmation require additional user interaction
Solution Approach 1:
The system performs partial automated verification by presenting only a subset of low-resolution images for user confirmation rather than requiring review of all detected events. This partial action approach maintains high throughput by automating the majority of detection while minimizing necessary user interaction for confirmation of potential CTCs.
Data Source
AI summary
An automated method for detecting circulating tumor cells in a microscopic image of a blood sample includes receiving, by a computer, a plurality of low-resolution images, each low resolution image providing a representation of the blood sample with one of a plurality of stains applied. The computer determines a threshold value for each of the plurality of stains based on the low resolution images and identifies a list of potential cells based on the threshold values. A gating process is performed on the list of potential circulating tumor cells to identify one or more likely or highly likely circulating tumor cells. The computer presents the subset of the low-resolution images in a verification interface comprising one or more components allowing a user to confirm that a respective low-resolution image included in the subset of the low-resolution images includes one or more circulating tumor cells.