Movable Imaging for Sample Handler Container Identification
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Solution Overview
Problem
Existing sample handlers in diagnostic laboratory systems face challenges in accurately and efficiently identifying and locating sample containers due to limited fields of view of fixed cameras, which can increase costs and processing time.
Innovation Solution
Incorporating a movable imaging device, such as a camera, mounted on a robot within the sample handler that can capture images of sample containers from multiple angles and orientations, combined with a classification algorithm using AI and neural networks to identify and classify the containers, including determining proper grip and detecting anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed cameras are used to capture images of sample containers, then the system structure is simple, but the field of view is limited and identification accuracy decreases
Solution Approach 1:
The imaging device is mounted on a movable robot that can dynamically adjust its position and orientation within the sample handler, transforming the static fixed camera system into a dynamic mobile imaging system. This allows the imaging device to access multiple angles and positions of sample containers, significantly improving identification accuracy while maintaining reasonable system complexity through automated movement control
Solution Approach 2:
A classifier system with trained machine learning models is introduced as an intermediary between the imaging device and the control system. This classifier processes images captured from various angles and orientations, enabling accurate sample container identification even when captured from non-standard positions, thereby resolving the contradiction between improved field of view and identification accuracy
2Measurement precision
If multiple fixed cameras are deployed to improve field of view, then identification accuracy improves, but system cost and complexity increase
Solution Approach 1:
Instead of deploying multiple static cameras simultaneously, the system uses a single imaging device mounted on a movable robot that sequentially captures images from multiple positions. This dynamic approach achieves comprehensive coverage equivalent to multiple fixed cameras but with reduced system complexity and lower cost
Solution Approach 2:
The movable imaging device serves multiple functions: it can capture images from various angles, track sample container movements, and adapt to different positions within the sample handler. This multi-functional design replaces the need for multiple specialized fixed cameras, reducing overall system complexity while maintaining high identification accuracy
3Productivity
If processing speed is increased to reduce processing time, then productivity improves, but identification accuracy may decrease
Solution Approach 1:
The robot moves the imaging device to optimal positions and captures images in advance before the sample containers need to be processed. This preliminary imaging action allows sufficient time for accurate image capture and classification without compromising subsequent processing speed, as the identification is completed before the containers move to the next stage
4Adaptability or versatility
If fixed imaging positions are used, then system complexity is low, but the ability to identify containers from all orientations is limited
Solution Approach 1:
The imaging system transitions from fixed positions to dynamic mobile positioning, allowing the imaging device to adapt to sample containers in any orientation or position within the sample handler. The robot's movement capabilities enable the system to capture images from multiple angles and adjust to varying container placements, significantly improving adaptability
Solution Approach 2:
The movable imaging device can autonomously navigate to appropriate positions and capture images of sample containers regardless of their orientation. The system self-adjusts to accommodate different container types and positions without requiring manual reconfiguration or fixed imaging stations for each possible orientation
Data Source
AI summary
A sample handler of a diagnostic laboratory system includes a plurality of holding locations configured to receive sample containers. An imaging device is movable within the sample handler and is configured to capture images of the holding locations and sample containers received therein. A controller is configured to generate instructions that cause the imaging device to move within the sample handler and capture images. A classification algorithm is implemented in computer code, and includes a trained model configured to classify objects in the captured images. Other sample handlers and methods of handling sample containers are disclosed.