Systems and methods to process electronic images to adjust attributes of the electronic images

A method for adjusting stains in whole slide images, the method comprising:

• receiving, as input, a portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains by:
• receiving, as further input, the stain type of the one or more stains;
• applying a trained machine learning system for predicting stain types to at least the portion of the whole slide image to obtain a predicted stain type of the one or more stains;
• and
• comparing the stain type to the predicted stain type to validate the stain type;
• retrieving, from a plurality of stored machine-learned transformations associated with a plurality of stain types, a machine-learned transformation associated with the stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space specific to the stain type;
• adjusting one or more attributes of the one or more stains in the second color space to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A system for adjusting stains in whole slide images, the system comprising:

• a data store storing a plurality of machine-learned transformations associated with a plurality of stain types;
• a processor;
• and
• a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising:
• receiving, as input, a portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from the plurality of machine-learned transformations stored in the data store, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed by classifying the plurality of pixels into background pixels and non-background pixels, and identifying one or more pixels of the plurality of pixels representing artifacts as artifact pixels, wherein the subset of pixels to be transformed include the non-background pixels and non-artifact pixels;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• adjusting one or more attributes of the one or more stains in the second color space to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A method for adjusting stains in whole slide images, the method comprising:

• receiving, as input, a portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from a plurality of stored machine-learned transformations associated with a plurality of stain types, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed by classifying the plurality of pixels into background pixels and non-background pixels, and identifying one or more pixels of the plurality of pixels representing artifacts as artifact pixels, wherein the subset of pixels to be transformed include the non-background pixels and non-artifact pixels;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• adjusting one or more attributes of the one or more stains in the second color space to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A system for adjusting stains in whole slide images, the system comprising:

• a data store storing a plurality of machine-learned transformations associated with a plurality of stain types;
• a processor;
• and
• a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising:
• receiving, as input, a portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from the plurality of machine-learned transformations stored in the data store, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• providing for display the subset of pixels in the second color space and a plurality of graphical user interface (GUI) control elements corresponding to a plurality of attributes of the one or more stains;
• receiving input associated with one or more of the plurality of GUI control elements;
• adjusting a respective one or more of the plurality of attributes of the one or more stains in the second color space based on the input to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A method for adjusting stains in whole slide images, the method comprising:

• receiving, as input, a portion of a whole slide image comprised of a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from a plurality of stored machine-learned transformations associated with a plurality of stain types, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• providing for display the subset of pixels in the second color space and a plurality of graphical user interface (GUI) control elements corresponding to a plurality of attributes of the one or more stains;
• receiving input associated with one or more of the plurality of GUI control elements;
• adjusting a respective one or more of the plurality of attributes of the one or more stains in the second color space based on the input to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A system for adjusting stains in whole slide images, the system comprising:

• a data store storing a plurality of machine-learned transformations associated with a plurality of stain types;
• a processor;
• and
• a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising:
• receiving, as input, a portion of a whole slide image comprised of at least one of a plurality of pixel patches that are randomly selected from a plurality of uniformly distributed patches across the whole slide image, the at least one of the plurality of pixel patches comprising a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from the plurality of machine-learned transformations stored in the data store, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• adjusting one or more attributes of the one or more stains in the second color space to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.

A method for adjusting stains in whole slide images, the method comprising:

• receiving, as input, a portion of a whole slide image comprised of at least one of a plurality of pixel patches that are randomly selected from a plurality of uniformly distributed patches across the whole slide image, the at least one of the plurality of pixel patches comprising a plurality of pixels in a first color space and including one or more stains;
• identifying a stain type of the one or more stains;
• retrieving, from a plurality of stored machine-learned transformations associated with a plurality of stain types, a machine-learned transformation associated with the identified stain type;
• identifying a subset of pixels from the plurality of pixels to be transformed;
• applying the machine-learned transformation to the subset of pixels to convert the subset of pixels from the first color space to a second color space, the second color space being specific to the identified stain type;
• adjusting one or more attributes of the one or more stains in the second color space to generate a stain-adjusted subset of pixels;
• converting the stain-adjusted subset of pixels from the second color space to the first color space using an inverse of the machine-learned transformation;
• and
• outputting a stain-adjusted portion of the whole slide image including at least the stain-adjusted subset of pixels in the first color space.