Complex system for contextual mask generation based on quantitative imaging

1. A method including:

obtaining specific quantitative image data captured via a quantitative imaging technique of a sample, the specific quantitative image data including, for each pixel of the specific quantitative image data, a quantitative parameter value and a pixel value, where the quantitative parameter value for the pixel is derived, at least in part, from the pixel value for the pixel;

determining a specific context mask for the specific quantitative image data by comparing the specific quantitative image data to previous quantitative image data for a previous sample via application of the specific quantitative image data to an input of a neutral network trained using constructed context masks generated based on the previous sample and the previous quantitative image data;

applying the specific context mask to the specific quantitative image data to determine a context value for the pixel, wherein the context value includes an expected dye concentration level at the pixel;

based on the pixel and the quantitative parameter value, determining a quantitative characterization for the context value; and

referencing the quantitative characterization against a structure integrity index to determine a condition of the sample.

2. The method of claim 1 , including altering the pixel value to indicate the context value.

3. The method of claim 1 , where the constructed context masks include a dye-contrast image captured of the previous sample after exposure of the previous sample to a contrast dye.

4. The method of claim 3 where the contrast dye includes a fluorescent material.

5. The method of claim 1 where the constructed context masks include operator input context designations.

6. The method of claim 5 , where the operator input context designations indicate that portions of an image depict an instance of a particular biological structure.

7. The method of claim 6 , where the context value indicates a determination that the pixel depicts, at least in part, an instance of the particular biological structure.

8. The method of claim 1 , where:

the quantitative imaging technique includes a non-destructive imaging technique; and

constructed context masks include images captured via a biologically-destructive imaging technique.

9. The method of claim 1 , where the quantitative imaging technique includes:

quantitative phase imaging;

gradient light interference microscopy;

spatial light inference microscopy;

diffraction tomography;

Fourier transform light scattering; or

any grouping of the foregoing.

10. The method of claim 1 , where obtaining the specific quantitative image data captured via a quantitative imaging technique includes capturing the pixel value via a pixel capture array positioned at a plane of a comparative effect generated by light rays traversing an objective and a processing optic.

11. A method including:

obtaining quantitative image data captured via quantitative imaging of a sample, the quantitative image data including multiple pixels, each of the multiple pixels including a respective quantitate parameter value, wherein the quantitate parameter value is derived from a raw pixel value of each pixel;

obtaining a constructed context mask for the sample, the constructed context mask including a context value for each of the multiple pixels;

creating an input-result pair by pairing the constructed context mask as a result to an input including the quantitative image data, wherein the context value includes an expected dye concentration level at the pixel;

applying the input-result pair to a neural network to adjust interneuron weights within the neural network;

determining a quantitative characterization for the context value; and

referencing the quantitative characterization against a structure integrity index to determine a condition of the sample.

12. The method of claim 11 , where applying the input-result pair to a neural network includes determining a deviation from the constructed context mask by a simulated context mask at an output of the neural network when the quantitative image data is applied as an input to the neural network when a test set of interneuron weights are present within the neural network.

13. The method of claim 12 , where determining the deviation includes determining a loss value between the constructed context mask and the simulated context mask to quantify the deviation.

14. The method of claim 13 , where applying the input-result pair to a neural network to adjust interneuron weights within the neural network includes adjusting the interneuron weights to achieve a reduction in the loss value according to an optimization algorithm.

15. The method of claim 12 , where the neural network includes a U-net neural network to support an image transformation operation between the quantitative image data and the simulated context mask.

16. The method of claim 11 , where the constructed context mask includes a dye-contrast image captured of the sample after exposure of the sample to a contrast dye.

17. The method of claim 11 , where:

the quantitative imaging includes a non-destructive imaging technique; and

constructed context mask includes an image captured via a biologically-destructive imaging technique.

18. A biological imaging device including:

a capture subsystem including:

an objective;

a processing optic positioned relative to the objective to generate a comparative effect from a light ray captured through the objective;

a pixel capture array positioned at a plane of the comparative effect;

a processing subsystem including:

memory configured to store:

raw pixel data from the pixel capture array; and

quantitative parameter values for pixels of the raw pixel data;

a neural network trained using constructed structure masks generated based on previous quantitative parameter values and previous pixel data;

a computed structure mask for the pixels;

a structure integrity index;

a processor in data communication with memory, the processor configured to:

determine the quantitative parameter values for the pixels based on the raw pixel data and the comparative effect, wherein the quantitate parameter values are derived from raw pixel values;

via execution of the neural network, determine the computed structure mask by assigning a subset of the pixels that represent portions of a selected biological structure identical mask values within the computed structure mask;

apply the computed structure mask to the raw pixel data to determine a context value for the pixel, wherein the context value includes an expected dye concentration level at the pixel;

based on ones of the quantitative parameter values corresponding to the subset of the pixels, determine a quantitative characterization of the selected biological structure; and

reference the quantitative characterization against the structure integrity index to determine a condition of the selected biological structure.

19. The biological imaging device of claim 18 , where:

the biological imaging device includes an assistive-reproductive-technology (ART) imaging device;

the biological structure includes a structure within a sperm; and

the condition includes a predicted success rate for zygote cleavage after fertilization of an ovum by the sperm.