Biomedical Object Segmentation via Automated Image Annotation
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Solution Overview
Problem
Current methods for detecting biomedical objects within biological samples are labor-intensive, prone to human biases, and result in inconsistent outcomes, making them difficult to automate effectively.
Innovation Solution
A system and method for biomedical object segmentation utilizing processors, memory modules, and machine-readable instructions that analyze image data to automatically recognize and annotate biomedical objects, including a training server for generating and storing identifiable characteristics of these objects for consistent identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used for biomedical object detection, then productivity and consistency are improved, but the system complexity increases
Solution Approach 1:
The patent replaces manual visual inspection by trained professionals with an automated computer-based system that uses image processing and machine learning algorithms to detect and annotate biomedical objects. This substitution of mechanical/manual processes with automated computational processes directly improves productivity while managing system complexity through software-based solutions.
Solution Approach 2:
The system creates a digital representation (copy) of the biological sample through image capture, then performs automated analysis on this copy. The trained model generates annotated copies of images with detected objects marked, allowing rapid reproduction of detection results without repeatedly examining original samples, thereby improving productivity.
2Measurement precision
If trained professionals perform visual detection, then detection accuracy is maintained, but loss of time increases
Solution Approach 1:
The system performs preliminary training of detection models using annotated images from trained professionals. This preliminary action creates a trained model that encapsulates expert knowledge, allowing the automated system to achieve comparable detection accuracy without requiring professionals to perform each detection task manually, thus reducing time loss while maintaining precision.
Solution Approach 2:
The system uses feedback from user corrections and validations to continuously improve the trained model. By incorporating feedback loops where professional annotations refine the automated detection algorithm, the system maintains high detection accuracy while progressively reducing the time required for analysis.
3Adaptability or versatility
If manual annotation is used, then flexibility in handling diverse objects is maintained, but productivity decreases
Solution Approach 1:
The system employs dynamic machine learning models that can adapt to different types of biomedical objects and imaging conditions. The trained model can be retrained and adjusted to handle diverse object types, providing flexibility comparable to manual annotation while achieving automated processing speeds, thus resolving the contradiction between adaptability and productivity.
Data Source
AI summary
In one embodiment, a system for biomedical object segmentation includes one or more processors; one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored on the one or more memory modules. The machine readable instructions cause the system to perform the following when executed by the one or more processors: receive image data of one or more biological constructs; analyze the image data to generate processed image data via a data analytics module to recognize biomedical objects; and automatically annotate the processed image data to indicate a location of one or more biological objects within the one or more biological constructs.