IP Library Granted Patent US 12,592,000
Granted Patent B2
US 12,592,000 · App. 18/060,358 · Granted Mar 31, 2026

Systems and methods for processing digital images to adapt to color vision deficiency

Inventors: Kristin Ruben (Boston, MA); Kyle Ondy (Millstone Township, NJ); Christopher Kanan (Pittsford, NY)
Assignee: Paige.AI, Inc.
G06T7/90G06T7/0012G06T2207/10056G06T2207/20081
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Quick Facts
Patent No.
US 12,592,000
App. No.
18/060,358
Granted
Mar 31, 2026
Kind
B2
Abstract

A computer-implemented method for processing medical images, the method including receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a patient, wherein the medical image is a stained histology image. The method may further include receiving a stain type associated with the one or more medical images and identifying a color vision deficiency for one or more users. Next the method may include identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users. Next the method may include applying a pixel transformation to each pixel within the one or more medical images. Lastly the method may include displaying the transformed one or more medical images to the one or more users.

Claims (59)

1 . A computer-implemented method for processing electronic medical images, comprising:

receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a subject, wherein the one or more medical images are stained histology images;

receiving a stain type associated with the one or more medical images;

identifying a color vision deficiency for one or more users from a plurality of color vision deficiencies;

identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users;

applying a pixel transformation to each pixel within the one or more medical images, wherein applying the pixel transformation includes:

applying the pixel transformation for each pixel of the one or more medical images based on an identified look-up table from a plurality of look-up tables, wherein the identified look-up table is determined based on the stain type and color deficiency of the one or more users; or

applying the pixel transformation for each pixel of the one or more medical images to convert the one or more medical images to an alternate color space, wherein the alternate color space is determined based on the stain type and color deficiency of the one or more users; and

displaying the transformed one or more medical images to the one or more users.

2 . The method of claim 1 , further comprising:

applying staining normalization to the one or more medical images prior to applying the pixel transformation.

3 . The method of claim 1 , further comprising:

saving the pixel transformation for a particular user for future use.

4 . The method of claim 1 , wherein color vision deficiency for one or more users is determined by administering a spectral sensitivity test to the one or more users.

5 . The method of claim 1 , wherein applying the pixel transformation includes for each pixel of the medical images, updating a corresponding red, green, blue (RGB) intensity value based on the identified look-up table.

6 . The method of claim 1 , wherein multiple pixel transformations may be created for a particular user and the user may select what pixel transformation to apply.

7 . The method of claim 1 , wherein applying the pixel transformation for each pixel of the one or more medical images based on the identified look-up table includes:

determining whether each pixel of the one or more medical images has an exact value in the identified look-up table; and

for pixels that do not have an exact value in the look-up table, applying an interpolation to generate red, green, blue (RGB) intensity values for the pixels; and

updating red, green, and blue intensity values for each pixel based on the identified look-up table values and the interpolated values.

8 . The method of claim 1 , wherein applying the pixel transformation includes:

applying artificial intelligence techniques to apply recoloring algorithms to enhance morphologies in the one or more medical images based on the color vision deficiency for one or more users.

9 . The method of claim 1 , wherein applying the pixel transformation for each pixel of the one or more medical images to convert the one or more medical images to an alternate color space includes applying a non-linear transformation to the pixels of the one or more medical images.

10 . A system for processing electronic digital medical images, the system comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a subject, wherein the one or more medical images are stained histology images;

receiving a stain type associated with the one or more medical images;

identifying a color vision deficiency for one or more users from a plurality of color vision deficiencies;

identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users;

applying a pixel transformation to each pixel within the one or more medical images, wherein applying the pixel transformation includes:

applying the pixel transformation for each pixel of the one or more medical images based on an identified look-up table from a plurality of look-up tables, wherein the identified look-up table is determined based on the stain type and color deficiency of the one or more users; or

applying the pixel transformation for each pixel of the one or more medical images to convert the one or more medical images to an alternate color space, wherein the alternate color space is determined based on the stain type and color deficiency of the one or more users; and

displaying the transformed one or more medical images to the one or more users.

11 . The system of claim 10 , further comprising:

applying staining normalization to the one or more medical images prior to applying the pixel transformation.

12 . The system of claim 10 , further comprising:

saving the pixel transformation for a particular user for future use.

13 . The system of claim 10 , wherein color vision deficiency for one or more users is determined by administering a spectral sensitivity test to the one or more users.

14 . The system of claim 10 , wherein applying the pixel transformation includes for each pixel of the medical images, updating a corresponding red, green, blue (RGB) intensity value based on the identified look-up table.

15 . The system of claim 10 , wherein multiple pixel transformations may be created for a particular user and the user may select what pixel transformation to apply.

16 . The system of claim 10 , wherein applying the pixel transformation for each pixel of the one or more medical images based on a look-up table includes:

determining whether each pixel of the one or more medical images has an exact value in the identified look-up table; and

for pixels that do not have an exact value in the look-up table, applying an interpolation to generate red, green, blue (RGB) intensity values for the pixels; and

updating red, green, and blue intensity values for each pixel based on the identified look-up table values and the interpolated values.

17 . The system of claim 10 , wherein applying the pixel transformation includes:

applying artificial intelligence techniques to apply recoloring algorithms to enhance morphologies in the one or more medical images based on the color vision deficiency for one or more users.

18 . The system of claim 10 , wherein applying the pixel transformation for each pixel of the one or more medical images to convert the one or more medical images to an alternate color space includes applying a non-linear transformation to the pixels of the one or more medical images.

19 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic digital medical images, the operations comprising:

receiving one or more of medical images of at least one pathology specimen, the pathology specimen being associated with a subject, wherein the one or more medical images are stained histology images;

receiving a stain type associated with the one or more medical images;

identifying a color vision deficiency for one or more users from a plurality of color vision deficiencies;

identifying a pixel transformation for the one or more medical images based on the stain type and color vision deficiency of the one or more users;

applying a pixel transformation to each pixel within the one or more medical images, wherein applying the pixel transformation includes:

applying the pixel transformation for each pixel of the one or more medical images based on an identified look-up table from a plurality of look-up tables, wherein the identified look-up table is determined based on the stain type and color deficiency of the one or more users; or

applying the pixel transformation for each pixel of the one or more medical images to convert the one or more medical images to an alternate color space, wherein the alternate color space is determined based on the stain type and color deficiency of the one or more users; and

displaying the transformed one or more medical images to the one or more users.

20 . The computer-readable medium of claim 19 , further comprising:

applying staining normalization to the one or more medical images prior to applying the pixel transformation.

Assignments (3)
RELEASE OF SECURITY INTEREST Recorded May 14, 2026
From: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
To: PAIGE.AI, INC.
Reel/Frame 075589/0752 →
SECURITY INTEREST Recorded Oct 21, 2025
From: PAIGE.AI, INC.
To: ARES CAPITAL CORPORATION, AS COLLATERAL AGENT
Reel/Frame 073216/0876 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 15, 2023
From: RUBEN, KRISTIN; ONDY, KYLE; KANAN, CHRISTOPHER
To: PAIGE.AI, INC.
Reel/Frame 062378/0542 →
Continuity (2)
Provisional Application 63291872 · Dec 20, 2021
Related Publication 20230196622A1 · Jun 22, 2023
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