Fast Brain Volumetric Segmentation from T1 MRI Scans

Anand, Ananya; Anand, Namrata

doi:10.1007/978-3-030-17795-9_30

Cited by 2 publications

(6 citation statements)

References 22 publications

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“…These differences present learning difficulties for NNs. Traditionally human brain segmentation was done manually, but this had several challenges hence the introduction of semi-automated and fully automated algorithms [5,9,10,13,18]. Deep learning algorithms have proven to be very effective in human brain segmentation [2,3,5].…”

Section: Motivationmentioning

confidence: 99%

“…Traditionally human brain segmentation was done manually, but this had several challenges hence the introduction of semi-automated and fully automated algorithms [5,9,10,13,18]. Deep learning algorithms have proven to be very effective in human brain segmentation [2,3,5]. In particular, CNNs have proved to be very effective in image segmentation.…”

Section: Motivationmentioning

confidence: 99%

“…Also, some component boundaries are not very clear, and in some cases, the boundaries are not available, which makes segmenting them very difficult [8,9,11]. Although traditional algorithms and existing software (such as Functional MRI of the brain FSL and Freesurfer [5,9]) offer ways of segmenting the human brain, they all generally suffer from either over-segmentation or under-segmentation. The former occurs when the resulting regions of interest are more than expected, while, the latter occurs when the segmentation algorithm produces insufficient regions of interest.…”

Section: Motivationmentioning

confidence: 99%

“…The majority of researchers focused more on fully-CNN and little research has been done in window sliding. The research studies that were based on fully connected CNN include , GANs [57,60,99] and several encoder-decoder based networks [5]. The encoderdecoder networks included amongst others the V-Net [94,129], Y-Net [24], U-Net [21,30,37,45,48,63,67,74,101,111,122] and ensemble [2,11].…”

Section: General Introduction 11 Introductionmentioning

confidence: 99%

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A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation

Vambe

Viriri

Gwetu

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The authors' contributions in the paper above are as follows:-1st Author : Concept formulation, literature review, design and implementation of algorithms and preparing and editing the write-up.-2nd and 3rd Authors : Concept improvement, providing advice, highlighting important considerations with respect to methods and techniques, and proof-reading manuscripts.I would like to express profound gratitude to my supervisors, that is, Prof. Serestina Viriri and Dr Mandlenkosi Gwetu, for their invaluable counsel, guidance and feedback during the work related to this dissertation. I would also like to extend heartfelt gratitude to several academics for their motivation and advice during my research. Last but not least I would like to thank my family for their continued support every step of the way. v 2.5D Two and a Half Dimensional. 5

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: General Introduction 11 Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation

Vambe

Viriri

Gwetu

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…T1, T2-FLAIR and T1-IR scans were used to train and evaluate the model using the dataset from MICCAI 2018 challenge. Similarly, Ahn et al [47] also worked on the segmentation task of eight brain regions. Their method used the attention module and CNN based approach.…”

Section: B Comparative Analysismentioning

confidence: 99%

Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

et al. 2020

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Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

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Fast Brain Volumetric Segmentation from T1 MRI Scans

Cited by 2 publications

References 22 publications

A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation

A Patch-Based Convolutional Neural Network for Localized MRI Brain Segmentation

Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

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