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Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI

Spiteri, Michaela, Guillemaut, Jean-Yves, Windridge, David, Avula, Shivaram, Kumar, Ram and Lewis, Emma (2019) Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI Neuroinformatics, 18 (1). pp. 151-162.

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Abstract

Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient’s treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Spiteri, Michaelamichaela.spiteri@surrey.ac.uk
Guillemaut, Jean-YvesJ.Guillemaut@surrey.ac.uk
Windridge, DavidD.Windridge@surrey.ac.uk
Avula, Shivaram
Kumar, Ramramesh.kumar@surrey.ac.uk
Lewis, EmmaE.Lewis@surrey.ac.uk
Date : 28 June 2019
DOI : 10.1007/s12021-019-09427-w
Copyright Disclaimer : © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords : MRI; POPCMS; Posterior foss; Longitudinal; Machine learning
Depositing User : Clive Harris
Date Deposited : 08 Jul 2019 12:45
Last Modified : 03 Feb 2020 14:37
URI: http://epubs.surrey.ac.uk/id/eprint/852220

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