A patch-based convolutional neural network for localized MRI brain segmentation.
Loading...
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Accurate segmentation of the brain is an important prerequisite for effective diagnosis, treatment
planning, and patient monitoring. The use of manual Magnetic Resonance Imaging
(MRI) segmentation in treating brain medical conditions is slowly being phased out in favour
of fully-automated and semi-automated segmentation algorithms, which are more efficient
and objective. Manual segmentation has, however, remained the gold standard for supervised
training in image segmentation. The advent of deep learning ushered in a new era in image
segmentation, object detection, and image classification. The convolutional neural network
has contributed the most to the success of deep learning models. Also, the availability of
increased training data when using Patch Based Segmentation (PBS) has facilitated improved
neural network performance. On the other hand, even though deep learning models have
achieved successful results, they still suffer from over-segmentation and under-segmentation
due to several reasons, including visually unclear object boundaries. Even though there have
been significant improvements, there is still room for better results as all proposed algorithms
still fall short of 100% accuracy rate. In the present study, experiments were carried out
to improve the performance of neural network models used in previous studies. The revised
algorithm was then used for segmenting the brain into three regions of interest: White Matter
(WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). Particular emphasis was placed
on localized component-based segmentation because both disease diagnosis and treatment
planning require localized information, and there is a need to improve the local segmentation
results, especially for small components. In the evaluation of the segmentation results, several
metrics indicated the effectiveness of the localized approach. The localized segmentation
resulted in the accuracy, recall, precision, null-error, false-positive rate, true-positive and F1-
score increasing by 1.08%, 2.52%, 5.43%, 16.79%, -8.94%, 8.94%, 3.39% respectively. Also,
when the algorithm was compared against state of the art algorithms, the proposed algorithm
had an average predictive accuracy of 94.56% while the next best algorithm had an accuracy
of 90.83%.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.