Surgeons may use hand gestures to manipulate MRI images in OR

January 10, 2013 − by wpengine − in Archived − Comments Off on Surgeons may use hand gestures to manipulate MRI images in OR

January 10, 2013

hand gestures chart

This
table shows hand gestures surgeons might use in the operating room to browse
and display medical images of the patient during an operation. Surgeons
routinely need to review medical images and records during surgery, but
stepping away from the operating table and touching a keyboard and mouse can
delay the surgery and increase the risk of spreading infection-causing bacteria
(Purdue University photo)

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WEST LAFAYETTE, Ind. — Doctors may soon be using a system
in the operating room that recognizes hand gestures as commands to tell a
computer to browse and display medical images of the patient during a surgery.

Surgeons routinely need to review medical images and
records during surgery, but stepping away from the operating table and touching
a keyboard and mouse can delay the procedure and increase the risk of spreading
infection-causing bacteria, said Juan Pablo Wachs, an assistant professor of
industrial engineering at Purdue University.

“One of the most ubiquitous pieces of equipment in
U.S. surgical units is the computer workstation, which allows access to medical
images before and during surgery,” he said. “However, computers and
their peripherals are difficult to sterilize, and keyboards and mice have been
found to be a source of contamination. Also, when nurses or assistants operate
the keyboard for the surgeon, the process of conveying information accurately
has proven cumbersome and inefficient since spoken dialogue can be
time-consuming and leads to frustration and delays in the surgery.”

Researchers are creating a system that uses depth-sensing
cameras and specialized algorithms to recognize hand gestures as commands to
manipulate MRI images on a large display. Recent research to develop the
algorithms has been led by doctoral student Mithun George Jacob.

Findings from the research were detailed in a paper published
in December in the Journal of the American Medical Informatics Association. The
paper was written by Jacob, Wachs and Rebecca A. Packer, an associate professor
of neurology and neurosurgery in Purdue’s College of Veterinary Medicine.

The researchers validated the system, working with
veterinary surgeons to collect a set of gestures natural for clinicians and
surgeons. The surgeons were asked to specify functions they perform with MRI
images in typical surgeries and to suggest gestures for commands. Ten gestures
were chosen: rotate clockwise and counterclockwise; browse left and right; up
and down; increase and decrease brightness; and zoom in and out.

Critical to the system’s accuracy is the use of
“contextual information” in the operating room – cameras observe the
surgeon’s torso and head – to determine and continuously monitor what the
surgeon wants to do.

“A major challenge is to endow computers with the
ability to understand the context in which gestures are made and to
discriminate between intended gestures versus unintended gestures,” Wachs
said. “Surgeons will make many gestures during the course of a surgery to
communicate with other doctors and nurses. The main challenge is to create
algorithms capable of understanding the difference between these gestures and
those specifically intended as commands to browse the image-viewing system. We
can determine context by looking at the position of the torso and the
orientation of the surgeon’s gaze. Based on the direction of the gaze and the
torso position we can assess whether the surgeon wants to access medical
images.”

The hand-gesture recognition system uses a camera
developed by Microsoft, called Kinect, which senses three-dimensional space.
The camera, found in consumer electronics games that can track a person’s hands,
maps the surgeon’s body in 3-D.

Findings showed that integrating context allows the
algorithms to accurately distinguish image-browsing commands from unrelated
gestures, reducing false positives from 20.8 percent to 2.3 percent.

“If you are getting false alarms 20 percent of the
time, that’s a big drawback,” Wachs said. “So we’ve been able to
greatly improve accuracy in distinguishing commands from other gestures.”

The system also has been shown to have a mean accuracy of
about 93 percent in translating gestures into specific commands, such as
rotating and browsing images.

 The algorithm takes into account what
phase the surgery is in, which aids in determining the proper context for
interpreting the gestures and reducing the browsing time.

“By observing the progress of the surgery we can tell
what is the most likely image the surgeon will want to see next,” Wachs
said.

The researchers also are exploring context using a mock
brain biopsy needle that can be tracked in the brain.

“The needle’s location provides context, allowing the
system to anticipate which images the surgeon will need to see next and
reducing the number of gestures needed,” Wachs said. “So instead of
taking five minutes to browse, the surgeon gets there faster.”

Sensors in the surgical needle reveal the position of its tip.

The research was supported by the Agency for Healthcare
Research and Quality, grant number R03HS019837.

Writer: Emil Venere, 765-494-4709, [email protected]

Sources: Juan Pablo Wachs, 765 496-7380, [email protected]

Note to
Journalists
: A video related to the research is available at http://youtu.be/jfgX3KGJdsk,
and the research paper is available by contacting Emil Venere, 765-494-4709,
[email protected]

 


ABSTRACT

Hand-gesture-based sterile interface for the
operating room using contextual cues for the navigation of radiological images

Mithun
George Jacob1, Juan Pablo Wachs1, Rebecca A Packer2

1School
of Industrial Engineering, Purdue University

2Departments
of Basic Medical Sciences and Veterinary Clinical Sciences, College of
Veterinary Medicine, Purdue University
 

This
paper presents a method to improve the navigation and manipulation of radiological
images through a sterile hand gesture recognition interface based on
attentional contextual cues. Computer vision algorithms were developed to
extract intention and attention cues from the surgeon’s behavior and combine
them with sensory data from a commodity depth camera. The developed interface
was tested in a usability experiment to assess the effectiveness of the new
interface. An image navigation and manipulation task was performed, and the
gesture recognition accuracy, false positives and task completion times were
computed to evaluate system performance. Experimental results show that gesture
interaction and surgeon behavior analysis can be used to accurately navigate,
manipulate and access MRI images, and therefore this modality could replace the
use of keyboard and mice-based interfaces.






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