SYSTEM AND METHODS FOR COLOR GAMUT NORMALIZATION FOR PATHOLOGY SLIDES

1 . A system for applying transformations to pathology slides, the system comprising at least a computing device, wherein the computing device is configured to:

receive a whole slide image;

generate a plurality of segments associated with the whole slide image as a function of one or more biological tissue type variabilities;

apply at least a segment-specific transformation to each segment of the plurality of segments, wherein the at least a segment-specific transformation is configured to increase a perceptual quality of the plurality of segments; and

display the plurality of segments through a graphical user interface as a function of a user interface data structure.

2 . The system of claim 1 , wherein the computing device is further configured to determine the at least a segment-specific transformation as a function of the plurality of segments.

3 . The system of claim 1 , wherein the computing device is communicatively connected to one or more scanners configured to capture the whole slide image from a pathology slide.

4 . The system of claim 1 , wherein:

applying the at least a segment-specific transformation to each segment of the plurality of segments comprises generating a processed image as a function of the at least a segment-specific transformation; and

the computing device is further configured to upload the processed image to a database.

5 . The system of claim 1 , wherein:

applying the at least a segment-specific transformation to each segment of the plurality of segments comprises generating a processed image as a function of the at least a segment-specific transformation; and

displaying the plurality of segments through the graphical user interface comprises visualizing the processed image.

6 . The system of claim 1 , wherein displaying the plurality of segments through the graphical user interface comprises displaying multiple views of a specimen within a Z-stack.

7 . The system of claim 1 , wherein generating the plurality of segments associated with the whole slide image as a function of one or more biological tissue type variabilities comprises generating the plurality of segments associated with the whole slide image using a computer vision model.

8 . The system of claim 1 , wherein:

the computing device is further configured to determine a current magnification level; and

generating the plurality of segments associated with the whole slide image using a computer vision model comprises segmenting the whole slide image based on semantic meaning of the current magnification level.

9 . The system of claim 8 , wherein:

the current magnification level comprises a 400× magnification level; and

segmenting the whole slide image based on semantic meaning of the current magnification level comprises:

training the computer vision model to segment the whole slide image into regions with different cell types; and

segmenting the whole slide image into regions with different cell types using the trained computer vision model.

10 . The system of claim 1 , wherein:

generating the plurality of segments associated with the whole slide image as a function of the one or more biological tissue type variabilities comprises determining a segment bounding path associated with the plurality of segments; and

the computing device is further configured to store the segment bounding path.

11 . A method applying transformations to pathology slides, the method comprising:

receiving, using at least a computing device, a whole slide image;

generating, using the at least a computing device, a plurality of segments associated with the whole slide image as a function of one or more biological tissue type variabilities;

applying, using the at least a computing device, at least a segment-specific transformation to each segment of the plurality of segments wherein the at least a segment-specific transformation is configured to increase a perceptual quality of the plurality of segments; and

displaying, using the at least a computing device, the plurality of segments through a graphical user interface as a function of a user interface data structure.

12 . The method of claim 11 , further comprising determining, by the at least a computing device, the at least a segment-specific transformation as a function of the plurality of segments.

13 . The method of claim 11 , wherein the computing device is communicatively connected to one or more scanners configured to capture the whole slide image from a pathology slide.

14 . The method of claim 11 , wherein:

applying the at least a segment-specific transformation to each segment of the plurality of segments comprises generating a processed image as a function of the at least a segment-specific transformation; and

the method further comprises uploading, by the at least a computing device, the processed image to a database.

15 . The method of claim 11 , wherein:

applying the at least a segment-specific transformation to each segment of the plurality of segments comprises generating a processed image as a function of the at least a segment-specific transformation; and

displaying the plurality of segments through the graphical user interface comprises visualizing the processed image.

16 . The method of claim 11 , wherein displaying the plurality of segments through the graphical user interface comprises displaying multiple views of a specimen within a Z-stack.

17 . The method of claim 11 , wherein generating the plurality of segments associated with the whole slide image as a function of one or more biological tissue type variabilities comprises generating the plurality of segments associated with the whole slide image using a computer vision model.

18 . The method of claim 11 , wherein:

the method further comprises determining, by the at least a computing device, a current magnification level; and

generating the plurality of segments associated with the whole slide image using a computer vision model comprises segmenting the whole slide image based on semantic meaning of the current magnification level.

19 . The method of claim 18 , wherein:

the current magnification level comprises a 400× magnification level; and

segmenting the whole slide image based on semantic meaning of the current magnification level comprises:

training the computer vision model to segment the whole slide image into regions with different cell types; and

segmenting the whole slide image into regions with different cell types using the trained computer vision model.

20 . The method of claim 11 , wherein:

generating the plurality of segments associated with the whole slide image as a function of the one or more biological tissue type variabilities comprises determining a segment bounding path associated with the plurality of segments; and

the method further comprises storing, by the at least a computing device, the segment bounding path.