Transforming multispectral images to enhanced resolution images enabled by machine learning

1. A computer-implemented method, comprising:

receiving an input image of a first biological sample, wherein the input image has a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel;

applying a trained artificial neural network to the input image, the trained artificial neural network trained using an image training data set including a plurality of image pairs, wherein each image pair of the plurality of image pairs includes a first image of a biological sample acquired when the biological sample is unstained, wherein the first image includes a first plurality of spectral images of the biological sample, and wherein each spectral image of the first plurality of spectral images includes data from a different wavelength band; and a second image of the biological sample acquired when the biological sample is stained;

generating an output image at a second spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and

outputting the output image.

2. The computer-implemented method of claim 1 , wherein the biological sample is unstained, and wherein the output image includes features of the biological sample after the biological sample is stained.

3. The computer-implemented method of claim 1 , further comprising:

determining a set of desired features associated with the biological sample; and

determining a specific number of spectral images based on the set of desired features, wherein the plurality of spectral images includes the specific number of spectral images.

4. The computer-implemented method of claim 1 , further comprising:

determining a set of desired features associated with the biological sample; and

determining a specific number of wavelength bands for each spectral image of the plurality of spectral images based on the set of desired features.

5. The computer-implemented method of claim 1 , wherein the trained artificial neural network is a convolutional neural network.

6. The computer-implemented method of claim 1 , wherein the output image is a red-green-blue (RGB) color image or a grayscale image.

7. The computer-implemented method of claim 1 , wherein the trained artificial neural network is further trained to increase a spatial resolution of an output image based on identifying one or more features represented in a first spectral image having higher resolution than the one or more features also represented in a second spectral image.

8. A system comprising:

a non-transitory computer-readable medium; and

one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to:

receive an input image of a first biological sample, wherein the input image has a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel;

apply a trained artificial neural network to the input image, the trained artificial neural network trained using an image training data set including a plurality of image pairs, wherein each image pair of the plurality of image pairs includes a first image of a biological sample acquired when the biological sample is unstained, wherein the first image includes a first plurality of spectral images of the biological sample, and wherein each spectral image of the first plurality of spectral images includes data from a different wavelength band; and a second image of the biological sample acquired when the biological sample is stained;

generate an output image at a second spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and

output the output image.

9. The system of claim 8 , wherein the biological sample is unstained, and wherein the output image includes features of the biological sample after the biological sample is stained.

10. The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:

determine a set of desired features associated with the biological sample; and

determine a specific number of spectral images based on the set of desired features, wherein the plurality of spectral images includes the specific number of spectral images.

11. The system of claim 8 , wherein the one or more processors are configured to execute further processor-executable instructions stored in the non-transitory computer-readable medium to:

determine a set of desired features associated with the biological sample; and

determine a specific number of wavelength bands for each spectral image of the plurality of spectral images based on the set of desired features.

12. The system of claim 8 , wherein the trained artificial neural network is a convolutional neural network.

13. The system of claim 8 , wherein the output image is a red-green-blue (RGB) color image or a grayscale image.

14. The system of claim 8 , wherein the trained artificial neural network is further trained to increase a spatial resolution of an output image based on identifying one or more features represented in a first spectral image having higher resolution than the one or more features also represented in a second spectral image.

15. A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:

receive an input image of a first biological sample, wherein the input image has a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel;

apply a trained artificial neural network to the input image, the trained artificial neural network trained using an image training data set including a plurality of image pairs, wherein each image pair of the plurality of image pairs includes a first image of a biological sample acquired when the biological sample is unstained, wherein the first image includes a first plurality of spectral images of the biological sample, and wherein each spectral image of the first plurality of spectral images includes data from a different wavelength band; and a second image of the biological sample acquired when the biological sample is stained;

generate an output image at a second spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and

output the output image.

16. The non-transitory computer-readable medium of claim 15 , wherein the biological sample is unstained, and wherein the output image includes features of the biological sample after the biological sample is stained.

17. The non-transitory computer-readable medium of claim 15 , further comprising processor-executable instructions configured to cause the one or more processors to:

determine a set of desired features associated with the biological sample; and

determine a specific number of spectral images based on the set of desired features, wherein the plurality of spectral images includes the specific number of spectral images.

18. The non-transitory computer-readable medium of claim 15 , further comprising processor-executable instructions configured to cause the one or more processors to:

determine a set of desired features associated with the biological sample; and

determine a specific number of wavelength bands for each spectral image of the plurality of spectral images based on the set of desired features.

19. The non-transitory computer-readable medium of claim 15 , wherein the trained artificial neural network is a convolutional neural network.

20. The non-transitory computer-readable medium of claim 15 , wherein the output image is a red-green-blue (RGB) color image or a grayscale image.