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A patch-based convolutional neural network for localized MRI brain segmentation.

dc.contributor.advisorViriri, Serestina.
dc.contributor.advisorGwetu, Mandlenkosi Victor.
dc.contributor.authorVambe, Trevor Constantine.
dc.date.accessioned2022-10-25T08:17:58Z
dc.date.available2022-10-25T08:17:58Z
dc.date.created2020
dc.date.issued2020
dc.descriptionMasters Degree. University of KwaZulu-Natal, Pietermaritzburg.en_US
dc.description.abstractAccurate 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%.en_US
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/21012
dc.language.isoenen_US
dc.subject.otherFully-automated segmentation algorithms.en_US
dc.subject.otherSemi-automated segmentation algorithms.en_US
dc.subject.otherPatch Based Segmentation.en_US
dc.subject.otherMagnetic resonance imaging.en_US
dc.subject.otherWhite matter.en_US
dc.subject.otherGrey matter.en_US
dc.subject.otherCerebrospinal fluid.en_US
dc.titleA patch-based convolutional neural network for localized MRI brain segmentation.en_US
dc.typeThesisen_US

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