Systems and methods for automatic cell identification using images of human epithelial tissue structure
Systems and methods for improving the image quality of images of epithelial tissue structures are disclosed. The systems include training a first cycle-GAN model and a second cycle-GAN model simultaneously, where the first cycle-GAN model is trained to remove noise from an image and the second cycle-GAN model is trained to learn the structure of the image. Additional systems and methods include deploying the trained cycle-GAN model to identify an unknown image segment and/or generate a protocol for following the identified skin care treatment recommendation for an identified image segment.
1 . A computer-implemented method, comprising:
training a first cycle-GAN model, wherein the first cycle-GAN model comprises a first generator GB 2 A, a second generator GA 2 B, a first discriminator DA, and a second discriminator DB 1 , and wherein training the first cycle-GAN model comprises:
receiving, by the first generator GB 2 A, a real image as a first input,
generating, by the first generator GB 2 A, a first synthetic image as a first output,
receiving, by the first discriminator DA, the real image and the first synthetic image from the first generator GB 2 A,
informing, by the first discriminator DA, a likelihood of each of the real image and the first synthetic image as being real or synthetic,
receiving, by the second generator GA 2 B, the real image and the first synthetic image,
generating, by the second generator GA 2 B, a first filtered synthetic image,
receiving, by the second discriminator DB 1 , the real image and the first filtered synthetic image from the second generator GA 2 B, and
informing, by the second discriminator DB 1 , a likelihood of each of the real image and the first filtered synthetic image as being real or synthetic, wherein the first cycle-GAN model learns noise through learning a translation from the first real image towards a first binary segmentation; and
training a second cycle-GAN model, wherein the second cycle-GAN model comprises a first generator GC 2 B, a second generator GB 2 C, a first discriminator DC, and a second discriminator DB 2 , and wherein training the second cycle-GAN model comprises:
receiving, by the first generator GC 2 B, a Gabor-filtered image as a second input,
generating, by the first generator GC 2 B, a second synthetic image as a second output, wherein the second output depends on the first output,
receiving, by the first discriminator DC, the Gabor-filtered and the second synthetic image from the first generator GC 2 B,
informing, by the first discriminator DC, a likelihood of each of the Gabor-filtered and the second synthetic image as being Gabor-filtered or synthetic,
receiving, by the second generator GB 2 C, the Gabor-filtered image and the second synthetic image,
generating, by the second generator GB 2 C, a second filtered synthetic image,
receiving, by the second discriminator DB 2 , the Gabor-filtered image and the second filtered synthetic image from the second generator GB 2 C, and
informing, by the second discriminator DB 2 , a likelihood of each of the Gabor-filtered image and the second filtered synthetic image as being Gabor-filtered or synthetic, wherein the second cycle-GAN model learns a structure of at least one of the real image or the Gabor-filtered image through learning a translation from the Gabor-filtered image towards a second binary segmentation.
2 . The computer-implemented method of claim 1 , wherein at least one of the real image, the Gabor-filtered image, the first synthetic image, or the second synthetic image is an epithelial structure image.
3 . The computer-implemented method of claim 2 , wherein the learned structure learned by the second cycle-GAN includes a position and integrity of membranes of the epithelial structure image.
4 . The computer-implemented method of claim 3 , wherein:
the position and integrity of membranes of the epithelial structure image include cell coordinates information, and
learning the structure of at least one of the real image or the Gabor-filtered image includes extracting values of epithelial tissue structure parameters.
5 . The computer-implemented method of claim 4 , wherein the epithelial tissue structure parameters are selected from cell area, perimeter, cell density, distribution of nearest neighbors, and distributions of distances between neighbors.
6 . The computer-implemented method of claim 1 , wherein the real image are acquired by non-invasive or minimally invasive imaging with cellular resolution.
7 . The computer-implemented method of claim 6 , wherein the non-invasive or minimally invasive imaging is selected from a group consisting of reflectance confocal microscopy, fluorescence confocal microscopy, fluorescence lifetime microscopy, multiphoton fluorescence microscopy, second harmonic generation microscopy, chemiluminescence imaging, photoacoustic microscopy, magnetic resonance imaging, optical coherence tomography, line-field optical coherence tomography and photo-thermal microscopy.