When surgeons remove cancer, one of the first questions is, “Did they get it all?”
Researchers from Rice University and the University of Texas MD Anderson Cancer Center have created
a new microscope that can quickly and inexpensively image large tissue sections, potentially during
surgery, to find the answer.
The microscope can rapidly image relatively thick pieces of tissue with cellular resolution,
and could allow surgeons to inspect the margins of tumors within minutes of their removal.
It was created by engineers and applied physicists at Rice and is described in a study published
in the journal Proceedings of the National Academy of Sciences.
The main goal of the surgery is to remove all the cancer cells, but the only way to
know if we got everything is to look at the tumor under a microscope.
Today, we can only do that by first slicing the tissue into extremely thin sections and
then imaging those sections separately.
This slicing process requires expensive equipment and the subsequent imaging of multiple slices
is time-consuming.
This project seeks to basically image large sections of tissue directly, without any slicing.
Rice’s deep learning extended depth-of-field microscope, or DeepDOF, makes use of an artificial
intelligence technique known as deep learning to train a computer algorithm to optimize
both image collection and image post-processing.
With a typical microscope, there’s a trade-off between spatial resolution and depth-of-field,
meaning only things that are the same distance from the lens can be brought clearly into
focus.
Features that are even a few millionths of a meter closer or further from the microscope’s
objective will appear blurry.
For this reason, microscope samples are typically thin and mounted between glass slides.
Slides are used to examine tumor margins today, and they aren’t easy to prepare.
Removed tissue is usually sent to a hospital lab, where experts either freeze it or prepare
it with chemicals before making razor-thin slices and mounting them on slides.
The process is time-consuming and requires specialized equipment and workers with skilled
training.
It is rare for hospitals to have the ability to examine slides for tumor margins during
surgery, and hospitals in many parts of the world lack the necessary equipment and expertise.
Current methods to prepare tissue for margin status evaluation during surgery have not
changed significantly since first introduced over 100 years ago.
By bringing the ability to accurately assess margin status to more treatment sites, the
DeepDOF has potential to improve outcomes for cancer patients treated with surgery.
DeepDOF uses a standard optical microscope in combination with an inexpensive optical
phase mask costing less than $10 to image whole pieces of tissue and deliver depths-of-field
as much as five times greater than today’s state-of-the-art microscopes.
Traditionally, imaging equipment like cameras and microscopes are designed separately from
imaging processing software and algorithms.
DeepDOF is one of the first microscopes that’s designed with the post-processing algorithm
in mind.
The phase mask is placed over the microscope’s objective to module the light coming into
the microscope.
The modulation allows for better control of depth-dependent blur in the images captured
by the microscope.
That control helps ensure that the deblurring algorithms that are applied to the captured
images are faithfully recovering high-frequency texture information over a much wider range
of depths than conventional microscopes.
DeepDOF does this without sacrificing spatial resolution.
In fact, both the phase mask pattern and the parameters of the deblurring algorithm are
learned together using a deep neural network, which allows to further improve performance.
DeepDOF uses a deep learning neural network, an expert system that can learn to make humanlike
decisions by studying large amounts of data.
To train DeepDOF, researchers showed it 1,200 images from a database of histological slides.
From that, DeepDOF learned how to select the optimal phase mask for imaging a particular
sample and it also learned how to eliminate blur from the images it captures from the
sample, bringing cells from varying depths into focus.
Once the selected phase mask is printed and integrated into the microscope, the system
captures images in a single pass and the machine learning algorithm does the deblurring.
DeepDOF can capture and process images in as little as two minutes.
The researchers have validated the technology and shown proof-of-principle.
A clinical study is needed to find out whether DeepDOF can be used as proposed for margin
assessment during surgery.
The researchers hope to begin clinical validation in the coming year.