A powerful tool for biophotonics in labs and clinics

The increasing importance of deep learning (DL), artificial intelligence (AI) and related computational methods to biophotonics and clinical practice was highlighted during a BIOS plenary event at SPIE Photonics West in January.Get more news about double deck compression forming machine,you can vist our website!

Aydogan Ozcan of UCLA chaired the session and is also received the Dennis Gabor Award in Diffractive Optics in recognition of his accomplishments in diffractive wavefront technologies, a field of central importance to the breakthroughs under discussion.

“The potential for deep learning to assist in image analysis and direct diagnosis is becoming well known,” commented Ozcan. “My topic at this plenary session is more focused on how DL can reconstruct images with better resolution or enable image transformations that are beyond our current understanding of physical models in computational microscopy. This new way of thinking can help us transform the existing tools used in, for example, histology.”

Traditional histology has involved the sectioning of tissue samples into thin layers for staining with specific chemical markers, in order to reveal the cells of interest. The drawback is that this takes time to carry out, requires the individual attention of specialist technicians, and moves the activity away from the immediate clinical treatment of a patient.

“Instead of sending tissues into a histology lab where a human technician works with chemicals and labels, DL could let us take label-free autofluorescence images of those tissue sections without any external agents and apply trained neural networks to mimic the stained version of the same tissue image,” said Ozcan. “You could potentially replace a whole field of histology with appropriate neural network models.”
Bypassing the chemistry in this way will make the process inherently faster. Instead of waiting for a day or a week, the result will be seen on-demand in minutes, making the overall workflow more cost effective. It should also help to democratize access to histology, removing the requirement for samples to be treated at fewer well-resourced medical centers.

“We call it call it virtual staining, and it has the advantages of being fast, cheap and repeatable,” Ozcan noted. “Chemical staining is a delicate procedure, especially for immunohistochemical staining for certain cancers, and pathologists know better than anyone that if you send 100 biopsies from 100 patients to a lab for advanced staining, then 30 percent of the staining will not lead to a definitive result. Pathologists see the results and know immediately that the staining has failed, or the tissue is distorted, and a week or two may have been lost.”
Environmentally friendly
As well as speed and sensitivity, virtual staining should lead to the elimination of unnecessary biopsies. A significant number of patients have to be recalled for a repeat procedure, after the original biopsy fails to deliver a definitive verdict.

“Traditional staining methods are destructive; they deplete the tissues, and the stained materials cannot then be reused,” said Aydogan Ozcan. “With virtual staining, we don’t do anything to the tissue sample except capture an image of it. I can carry out further analysis or repeat an earlier one because we have not destroyed or lost the tissues, another major advantage.”

Since the tissues are still intact and unchanged, another different molecular analysis or virtual chemical staining can be carried out on the same sample, something beyond the capabilities of traditional methods. At present, if a diagnostician wishes to examine tissues with different contrasts or stains then fresh sections of tissue must be obtained, a methodology accepted in conventional microscopy but which deep learning can get around.

“The impact of deep neural nets as a means to perform some unique transformations within the microscopy optical microscopy domain will be significant, not least from the perspective of virtual staining and the concept of mimicking the staining process,” concluded Ozcan. “Plus, it’s a green technology. The staining processes today wastes millions of gallons of water a year globally, and the staining chemicals can be very toxic. Virtual staining is dye free and we do not have to create waste, making it attractive from the perspective of environmental protection and sustainability too.”