US 2022/0044074 A1Published application

Efficient Training and Accuracy Improvement of Imaging Based Assay

AssigneeEssenlix Corporation

InventorsXing Li, Wu CHOU, Stephen Y. CHOU, Wei DING

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Abstract

The present disclosure relates to devices, apparatus and methods of improving the accuracy of image-based assay, that uses imaging system having uncertainties or deviations (imperfection) compared with an ideal imaging system. One aspect of the present invention is to add the monitoring marks on the sample holder, with at least one of their geometric and/optical properties of the monitoring marks under predetermined and known, and taking images of the sample with the monitoring marks, and train a machine learning model using the images with the monitoring mark.

Claims37

Dependent claims

A method of training a machine learning model for an image based assay, wherein the assay is, during a test, imaged by a low-quality imaging system, comprising:

having a first sample forming a thin layer on an imaging area of a first sample holder, wherein the first sample holder is a marked sample holder comprising one or more monitoring marks on the imaging area;
having a second sample forming a thin layer on an imaging area of a second sample holder, wherein the second sample holder is a marked sample holder that comprising an identical one or more monitoring marks on the imaging area of the second sample holder to the one or more monitoring marks on the first sample holder;
imaging, using a low-quality imaging system, a first image of the sample on the imaging area of the first sample holder;
imaging;
using a high-quality imaging system, a second image of the sample on the imaging area of the second sample holder;
correcting an imperfection in the first image using the monitoring marks, generating a first corrected image;
correcting an imperfection in the second image using the monitoring marks, if the second image has an imperfection, generating a second corrected image;
and
training a machine learning model using the first corrected image, the second corrected image and the monitoring marks, generating a trained model, wherein a geometric property and optionally an optical property of the one or more monitoring marks imaged under an ideal imaging system is predetermined and known;
wherein a low quality imaging system comprise more imperfection than a high quality imaging system.

7 more elements

A method to train a machine learning model for image-based assays, the method comprising:

receiving a first image, captured by a first optical sensor, of a sample holder containing a sample, wherein the sample holder is fabricated with a standard of patterned structural elements at predetermined positions;
identifying a first region in the first image based on locations of one or more structural elements of the patterned structural elements in the first image;
determining a spatial transform associated with the first region based on a mapping between the locations of the one or more structural elements in the first image and predetermined positions of one or more structural elements in the sample holder;
applying the spatial transform to the first region in the first image to calculate a transformed first region;
and
training the machine learning model using the transformed first image.

3 more elements

A method for converting an assay image using a machine learning model, the method comprising:

receiving a first image, captured by a first optical sensor, of a sample holder containing a sample, wherein the sample holder is fabricated with a standard of patterned structural elements at predetermined positions;
identifying a first region in the first image based on locations of one or more structural elements of the patterned structural elements in the first image;
determining a spatial transform associated with the first region based on a mapping between the locations of the one or more structural elements in the first image and predetermined positions of one or more structural elements in the sample holder;
applying the spatial transform to the first region in the first image to calculate a transformed first region;
and
applying the machine learning model to the transformed first region in the first image to generate a second region.

3 more elements

An image-based assay system, comprising:

a database system to store images;
and
a processing device, communicatively coupled to the database system, to:
receive a first image, captured by a first optical sensor, of a sample holder containing a sample, wherein the sample holder is fabricated with a standard of patterned structural elements at predetermined positions;
identify a first region in the first image based on locations of one or more structural elements of the patterned structural elements in the first image;
determine a spatial transform associated with the first region based on a mapping between the locations of the one or more structural elements in the first image and predetermined positions of one or more structural elements in the sample holder;
apply the spatial transform to the first region in the first image to calculate a transformed first region;
and
train the machine learning model using the transformed first image.

6 more elements

An image-based assay system for converting an assay image using a machine learning model, comprising:

a database system to store images;
and
a processing device, communicatively coupled to the database system, to:
receive a first image, captured by a first optical sensor, of a sample holder containing a sample, wherein the sample holder is fabricated with a standard of patterned structural elements at predetermined positions;
identify a first region in the first image based on locations of one or more structural elements of the patterned structural elements in the first image;
determine a spatial transform associated with the first region based on a mapping between the locations of the one or more structural elements in the first image and predetermined positions of one or more structural elements in the sample holder;
apply the spatial transform to the first region in the first image to calculate a transformed first region;
and
apply the machine learning model to the transformed first region in the first image to generate a second region.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/431,345Filed 25 Nov 2020
Priority: 25 Nov 2019
Published: 10 Feb 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification24 codes

G06F 18/2148
Generating training patterns; Bootstrap methods, e.g. bagging or boosting: characterised by the process organisation or structure, e.g. boosting…
G06N 3/042
Architecture, e.g. interconnection topology: knowledge-based neural networks; Logical representations of neural networks
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]

Family

Application62/940,242Us provisional application · filed 25 Nov 2019
US 11,593,590 B2Related patent
US 2022/0044074 A1This patent

From the US filing history. Foreign family members aren’t listed.

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