A Model for Object Recognition in Liver Resection Surgery
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2020
Författare
AL-MALEH, CHRISTIAN
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Laparoscopic liver resection is a safer alternative to open surgery for treatment of
liver cancer, a disease which claims almost 800 000 lives every year. The procedure
involves making small incisions in the abdomen where instruments and a camera,
called a laparoscope, are inserted. One of the major drawbacks of laparoscopic
surgery is the restricted view and orientation, as well as lack of haptic feedback.
Incorporating Augmented Reality, or AR, in the laparoscopic view is a proposed
method of facilitating the navigation. This work extends a previous model for
projecting information from 3D to 2D and vice versa using reference points, which
correctly visualizes the shape, angle and size of a tumor in AR in the 2D laparoscopic
view. To enable the 2D-to-3D projection, two object recognition models based on
image segmentation and edge detection, respectively, were developed where white
reference objects were distinguished from the darker tones of the liver tissue. The
positions of the reference objects were then measured. The latter model, albeit
effective given certain frames, failed to identify fiducials over the course of a test film.
Since the process of image segmentation is computationally heavy, it was localized
to an area of interest in a given frame, reducing the algorithm’s runtime. Statistical
error estimation was used to validate the positions found by this recognition model.
The average position error produced was between 1 to 5 pixels, where the frames had
a pixel height of 1080. Future work involves combining the recognition algorithm
with the projection model to examine the effect of the deviations of the estimated
positions in the 2D laparoscopic view.
Beskrivning
Ämne/nyckelord
object recognition, image segmentation, edge detection, laparscopic, liver cancer, surgery, augmented reality.