Image analysis systems and related methods

Patent metadata

Field	Value
Publication number	US-10061972-B2
Application number	US-201715433656-A
Country	US
Kind code	B2
Filing date	Feb 15, 2017
Priority date	May 28, 2015
Publication date	Aug 28, 2018
Grant date	Aug 28, 2018

Abstract

Embodiments disclosed herein are directed to systems and methods for determining a presence and an amount of an analyte in a biological sample. The systems and methods for determining the presence of an analyte utilize a plurality of images of a sample slide including multiple fields-of-view having multiple focal planes therein. The systems and methods utilize algorithms configured to balance the color and grayscale intensity of the plurality of images and based thereon determine if the plurality of images contain the analyte therein.

First claim

What is claimed is:

1. A system for determining a presence of an analyte in blood, the system comprising:
at least one memory storage medium configured to store a plurality of images of a sample slide, the plurality of images including,
a plurality of fields-of-view, each including a unique x and y coordinate of the sample slide; and
a plurality of focal planes, each having a unique z coordinate of the sample slide;

at least one processor operably coupled to the at least one memory storage medium, the at least one processor being configured to,
determine and apply a white balance transform to each of the plurality of images effective to produce a plurality of color-corrected images;
determine and apply an adaptive grayscale transform to each of the plurality of images to provide an adaptive grayscale intensity image for each of the plurality of images;
detect and identify one or more candidate objects in the color-corrected images and the adaptive grayscale intensity images, and the at least one processor is further configured to,
perform an adaptive thresholding operation on the adaptive grayscale intensity images and output one or more candidate objects based thereon;
cluster the one or more detected candidate objects into clusters including one or more adjacent candidate objects per cluster and associate clusters of detected candidate objects indicating that a cluster of one or more adjacent candidate objects are a single candidate object and output locations of the clusters of one or more adjacent candidate objects, the locations including one or more image patches containing the one or more adjacent candidate objects;
locate the focal plane having a best focus for each single candidate object;
determine attributes of each single candidate object in the focal plane having the best focus for each single candidate object;
filter each single candidate object based at least in part on one or more determined attributes; and
extract and output one or more image patches each containing at least one filtered single candidate object of the one or more candidate objects.

2. The system of claim 1 , further comprising:
a threshold determination module configured to determine a local adaptive threshold of grayscale intensity for one or more regions in the adaptive grayscale intensity images and is operably coupled to,
an image preprocessing module that is configured to receive one or more adaptive grayscale intensity images therefrom; and
a white blood cell detection module that is configured to receive a white blood cell detection mask therefrom, the white blood cell detection mask including information about locations of white blood cells in the plurality of fields-of-view and plurality of focal planes.

3. The system of claim 2 , wherein the threshold determination module is configured to,
determine the local adaptive threshold of grayscale intensity for at least some windows of a plurality of windows in the plurality of fields-of-view and plurality of focal planes in the adaptive grayscale images, including at least some windows containing one or more candidate objects therein, by locally estimating a noise floor of the at least some of the windows.

4. The system of claim 3 , wherein the threshold determination module is configured to locally estimate the noise floor of at least some of the windows by determining a median grayscale intensity value in each of the at least some windows in the adaptive grayscale intensity images, discounting any variations in the median grayscale intensity value due to presence of white blood cells, to produce the local adaptive threshold in each of the at least some windows.

5. The system of claim 4 , wherein the threshold determination module is configured to,
receive information noting a presence and location of white blood cells within one or more identified windows of a field-of-view of the plurality of fields-of-view;
when a white blood cell is indicated as present, replace pixels containing the white blood cell in a specific region of the one or more identified windows of the field-of-view with a replacement median grayscale intensity value determined from all pixels in the field-of-view;
determine a local median grayscale intensity value for all pixels in each of the one or more identified windows after white blood cell containing pixels have been replaced by the replacement median grayscale intensity value; and
output the local adaptive threshold for each of the one or more identified windows based on the local median grayscale intensity value therein.

6. The system of claim 5 , wherein the threshold determination submodule is operably coupled to a blob identification submodule configured to receive and apply the local adaptive threshold to each of the adaptive grayscale intensity images corresponding to the at least some windows.

7. The system of claim 2 , further including a blob identification submodule operably coupled to the threshold determination submodule, the blob identification submodule is configured to receive one or more local adaptive thresholds from the threshold determination submodule.

8. The system of claim 7 , wherein the blob identification submodule is configured to apply the local adaptive threshold to corresponding image patches of the plurality of fields-of-view and determine a presence of one or more candidate objects in the corresponding image patches, the one or more candidate objects having a grayscale intensity below the local adaptive threshold.

9. The system of claim 1 , further including a blob detection submodule, wherein the blob detection submodule is configured to,
invert a brightness of the adaptive grayscale intensity images to produce a plurality of inverted grayscale intensity images;
determine the local adaptive threshold of grayscale intensity based upon the plurality of inverted grayscale intensity images; and
determine the presence of the one or more candidate objects in each image patch above the local adaptive threshold based upon a brightness threshold of the plurality of inverted grayscale intensity images.

10. The system of claim 1 , wherein;
the at least one processor is configured to determine the focal plane with a highest focus score for each image patch having each single candidate object;
the at least one processor is configured to select and output the respective focal planes with the highest focus score for each candidate object; and
the at least one processor is configured to identify one or more of a roundest blob, a darkest blob, or a specifically sized blob in the focal plane having the highest focus score for each single candidate object and assign one or more of the roundest blob, the darkest blob, or the specifically sized blob as a candidate object of interest.

11. The system of claim 1 , wherein the at least one processor is configured to output determined attributes of each single candidate object in the focal plane having the best focus for each single candidate object to classify each single candidate object as an artifact or candidate object based on the one or more determined attributes.

12. The system of claim 11 , wherein the at least one processor is further configured as or includes an artifact classifier configured to score each single candidate object based at least in part on one or more determined attribute.

13. The system of claim 12 , wherein the at least one processor is configured to discard single candidate objects with a score below a threshold score and retain each single candidate object having a score above the threshold score.

14. The system of claim 13 , wherein the at least one pro

Assignees

• Tokitae Llc

Inventors

• Champlin Cary Richard
• Delahunt Charles
• Horning Matthew P
• Hu Liming
• Mcguire Shawn Keith
• Mehanian Courosh
• Thompson Clay Matthew
• Wilson Benjamin K

Classifications

• G06T7/0012 Primary

  Biomedical image inspection · CPC title

• G06V20/695

  Preprocessing, e.g. image segmentation · CPC title

• G06V10/56

  relating to colour · CPC title

• G06K9/4652

  Physics · mapped topic

• G06K9/0014 Primary

  Physics · mapped topic