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2019 | OriginalPaper | Buchkapitel

Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data

verfasst von : Hafsa Moontari Ali, M. Shamim Kaiser, Mufti Mahmud

Erschienen in: Brain Informatics

Verlag: Springer International Publishing

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Abstract

Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.

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Titel: Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data
verfasst von: Hafsa Moontari Ali
M. Shamim Kaiser
Mufti Mahmud
Verlag: Springer International Publishing
Buch: Brain Informatics
Print ISBN: 978-3-030-37077-0

Electronic ISBN: 978-3-030-37078-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-37078-7_14

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