Interferometry Mass Detection Background Subtraction
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Solution Overview
Problem
Current techniques for detecting small mass changes in objects, particularly cells, using interferometry are limited by background noise and computational limitations, requiring lengthy processes that delay treatment decisions in cancer therapy, necessitating improved methods for rapid and accurate detection of therapeutic effectiveness.
Innovation Solution
The implementation of novel image processing techniques, including initial flattening, background subtraction using polynomial functions, and machine learning for classification and autofocus, enables rapid detection of small mass changes in cells, allowing for high-throughput analysis and identification of optimal therapeutics within hours rather than days or weeks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interferometry techniques are used to detect mass changes, then measurement precision can be achieved, but the process is time-consuming and requires days or weeks to assess therapeutic effectiveness
Solution Approach 1:
The patent applies preliminary background subtraction and flattening corrections to interferometry images before mass change analysis. By pre-processing images to remove background noise and optical artifacts, the system eliminates time-consuming post-processing steps and enables rapid therapeutic assessment while maintaining measurement precision
Solution Approach 2:
The patent extracts and removes background components from interferometry images using polynomial fitting and subtraction techniques. By separating the background signal from the cell mass signal, the system achieves rapid processing without sacrificing the precision needed to detect small mass changes
2Measurement precision
If traditional image processing techniques are used, then computational accuracy can be maintained, but background noise from media and plates limits resolution
Solution Approach 1:
The patent converts the harmful background noise into a measurable signal by modeling it with polynomial functions. The background, which was previously a source of error, is now explicitly characterized and subtracted, transforming it from a harmful factor into a controlled element that improves overall measurement accuracy
Solution Approach 2:
The patent introduces polynomial fitting functions as intermediary elements between the raw interferometry images and the final mass change measurements. These mathematical models act as mediators that separate background contributions from cell signals, enabling accurate mass detection despite the presence of background noise from media and plates
3Productivity
If high-throughput analysis is implemented to speed up therapeutic assessment, then productivity increases, but computational limitations arise
Solution Approach 1:
The patent replaces complex computational image processing algorithms with simplified polynomial fitting and subtraction methods. This substitution reduces computational complexity and enables high-throughput analysis while maintaining the ability to detect small mass changes, thereby increasing productivity without overwhelming computational resources
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
These techniques enable statistically significant mass change detection in hours, improving the speed and accuracy of therapeutic assessment, facilitating timely treatment decisions and potentially enhancing cancer treatment outcomes by identifying effective therapeutics and monitoring cell responses.
Implementation Method 1
Interferometry is a microscopy-based technique involving transforming a phase difference in light (e.g., occurring from changes in the speed of light from traveling through different media) into an intensity difference
Implementation Method 2
based on the index of refraction, the dry mass of an object (e.g., a cell) can be determined
Data Source
AI summary
A computer-implemented method of using interferometry to detect mass changes of objects in a solution includes obtaining a time series of images using interferometry, and performing background correction on each image by classifying pixels of the image as background pixels or object pixels, fitting only the background pixels of the image to a function to generate a background fitted function, and subtracting the background fitted function from the image to generate a background corrected image. The method includes performing segmentation on the background corrected images to resolve boundaries of one or more objects, performing motion tracking on the objects to track changes in position of the objects, determining respective masses of the motion tracked objects and determining, for each image in the time series, an aggregate mass based on the respective masses to determine whether the aggregate mass of the motion tracked objects is increasing or decreasing.