Medical practitioners' aim while performing surgery is to remove all cancer cells from the patient’s body. However, in order to do that, a tumor needs to be seen through a microscope. For now, this can only be done by slicing the tissue into exceptionally thin sections and then reviewing one section at a time. This method turns out to be costly and time-taking.
To solve the above-mentioned problem, a recent study has undertaken the task of observing large image sections without the requirement of slicing them up. This newly developed microscope may help in boosting the Clinical Microscopes Market as it will enable a doctor to image the large sections of tissue quickly and inexpensively while in surgery. DeepDOF (new microscope) has the capability to increase outcomes for cancer patients who go under surgery.
It uses a combination of a standard optical microscope and an economical optical phase mask, which costs less than $10. The technology helps in imaging pieces of tissue wholly. Also, it has the ability to deliver depths-of-fields five times greater than what is provided by current state-of-the-art microscopes.
Methods used to prepare tissue for margin status evaluation during surgery have not seen much development from the time the method was introduced. Today slides are used to examine a tumor margin, which isn’t an easy feat to accomplish. Slides are prepared by sending removed tissues to a hospital lab, wherein experts freeze the tissues or prepare them with chemicals. After this, they make razor-thin slices on tissue and mount them on the slides. This turns out to be a rather time-taking process and requires workers with skills and specialized equipment. Most hospitals lack both of the variables, necessary equipment as well as expertise.
DeepDOF (Deep learning extended depth-of-field microscope) allows surgeons to inspect margins of tumor minutes after they are removed. It uses an artificial intelligence technique called deep learning to trains the computer algorithm. Through this, optimization occurs in both image collection and image-post processing.
Conventional imaging equipment is created separately from imaging processing software and algorithms. DeepDOF is one of the first to have been designed considering post-processing algorithms. It uses a neural network, an expert system that studies a large amount of data to make human-like decisions.
Researchers trained the new system by showing it 1200 images from historical slides. With the help of these slides, DeepDOF learned the method of electing the optimal phase mask for imaging a sample. It also learned to eliminate blurs from the images captured, leading to cells with varying depths coming into focus. The selected phase mask is printed and simultaneously integrated into the microscope. Then the system captures all the images while the machine learning algorithm works on deblurring them.
Researchers have stated that, while this is a significant development in the field of microscopy. Further clinical studies needed to take on the task of determining whether DeepDOF can be used as stated by researchers for margin assessment during surgery or not.
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