Assay Plate Imaging for Focus Stacking of Moving Organisms
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Solution Overview
Problem
Existing methods struggle to automatically and efficiently capture and evaluate assay plates with non-flat supports and moving organisms, such as insects and nematodes, for high-throughput screening of active ingredients, due to focus issues and time constraints.
Innovation Solution
A system and method utilizing a camera with adjustable focal planes, illumination, and well positioning, combined with image processing and neural networks, to acquire and analyze macroscopic images of organisms in assay plates, enabling focus stacking and rapid classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single focal plane image is used for capturing organisms on non-flat supports, then the image acquisition is simple and fast, but the depth of field is insufficient and most regions are out of focus
Solution Approach 1:
The image acquisition process is segmented into multiple discrete focal plane captures, with the optical system systematically adjusting to different focus distances to capture images at multiple depth levels. This segmentation of the continuous depth range into discrete focal planes enables complete coverage of the non-flat support surface while maintaining manageable system complexity through automated sequencing
Solution Approach 2:
The system transitions from capturing a single two-dimensional image to acquiring a three-dimensional stack of images at different focal planes. This dimensional extension along the depth axis (z-direction) enables comprehensive capture of organisms on non-flat supports by adding the depth dimension to the traditional x-y plane imaging
2Reliability
If focus stacking with multiple images is used to achieve complete focus, then the image quality is improved, but the measurement time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-planning and pre-positioning the focal plane sequence before actual image capture begins. The optical system is pre-configured with the complete set of focal distances that will be traversed, and the automation controller is pre-programmed with the acquisition sequence, enabling rapid execution without real-time decision delays
Solution Approach 2:
The image acquisition process maintains continuous useful action by systematically traversing through the focal plane stack without interruptions or re-positioning delays. The optical system continuously adjusts focal distance while the detector continuously captures images, creating an unbroken sequence of data acquisition that maximizes throughput efficiency
3Productivity
If the measurement time per well is reduced for high-throughput screening, then the productivity is improved, but the image quality and classification accuracy may deteriorate
Solution Approach 1:
The system replaces manual or semi-automatic image evaluation with automated optical and computational systems. Machine learning algorithms and computer vision systems automatically classify organisms based on the captured images, eliminating the need for time-consuming manual analysis while maintaining or improving classification accuracy through consistent, objective criteria
Solution Approach 2:
The system optimizes acquisition parameters such as the number of focal planes, exposure time, and illumination intensity to achieve the minimum necessary image quality for accurate classification. By carefully selecting and adjusting these parameters, the system captures sufficient detail for reliable organism identification while minimizing total acquisition time per well
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and reliable determination of active ingredient effects on living organisms, allowing high-throughput screening with accurate identification and classification of insects and nematodes, even on non-flat supports, within a short measurement time.
Implementation Method 1
Image analysis requires an in-focus image of an entire well. This is achieved through the use of a combination of continuous shooting of the well and an image processing algorithm, so-called 'focus stacking'.
Implementation Method 2
a camera having an optical system, wherein the camera is arranged over the well and serves to acquire multiple macroscopic images having different focal planes from the well
Data Source
AI summary
The invention relates to an automated system and a method for determining the effect of active ingredients on organisms such as insects, Acariformes and nematodes in an assay plate containing wells with the aid of image-acquisition and image-analysis technology. The invention allows the characterization of the population of the organisms at multiple time points.