Abstract:
"
Statistics have stated out that more than 60% of people have experienced a lower back
pain at some point in their life. Disc herniation is one of the greatest contributors
towards lower back pain and more than 95% herniated discs occurs in the lumbar
spine. During clinical process the radiologists have to examine the MRI to diagnose a
disc herniation and it is not just one case where the radiologists have to deal with, there
might be multiple cases to be examined and the doctors are left with cogitating and
envisaging. Segmentation of medical images will be really useful for the diagnosis
process of spine pathologies, studies of anatomical structures, for surgical intervention
and for evaluation of various therapies but manual segmentation of medical images by
the experts will cost a lot of time, effort and discipline.
This dissertation is a result of the project on building an automatic semantic
segmentation system. These models are built and embedded into computer aided
systems to automate the process of segmentation without the human intervention and
to identify minor alterations through pixel level where the human eye is not capable of
detecting. The utilized dataset for this project contains 1545 slices of MRI images of
the intervertebral disc from the axial view along with the annotated labels by the expert
radiologists. A series of 2D convolutional neural network was used with the UNet
architecture to achieve the semantic segmentation process. The segmentations are
evaluated using the dice coefficient and the Jaccard index.
This system works only for the intervertebral discs from the axial view and its results
are up to the standards. When compared to existing systems, the segmentation process
is performed equally or better and the user interaction of this system is facilitated
through a web application embedded with other features."
