Literature DB >> 26183648

Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis.

Pawel Szwarc1, Jacek Kawa2, Marcin Rudzki1, Ewa Pietka1.   

Abstract

In this paper a novel multi-stage automatic method for brain tumour detection and neovasculature assessment is presented. First, the brain symmetry is exploited to register the magnetic resonance (MR) series analysed. Then, the intracranial structures are found and the region of interest (ROI) is constrained within them to tumour and peritumoural areas using the Fluid Light Attenuation Inversion Recovery (FLAIR) series. Next, the contrast-enhanced lesions are detected on the basis of T1-weighted (T1W) differential images before and after contrast medium administration. Finally, their vascularisation is assessed based on the Regional Cerebral Blood Volume (RCBV) perfusion maps. The relative RCBV (rRCBV) map is calculated in relation to a healthy white matter, also found automatically, and visualised on the analysed series. Three main types of brain tumours, i.e. HG gliomas, metastases and meningiomas have been subjected to the analysis. The results of contrast enhanced lesions detection have been compared with manual delineations performed independently by two experts, yielding 64.84% sensitivity, 99.89% specificity and 71.83% Dice Similarity Coefficient (DSC) for twenty analysed studies of subjects with brain tumours diagnosed.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Brain tumour; Computer aided diagnosis; Image segmentation; Magnetic resonance imaging; Perfusion maps

Mesh:

Year:  2015        PMID: 26183648     DOI: 10.1016/j.compmedimag.2015.06.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  6 in total

Review 1.  Diagnostic Clinical Trials in Breast Cancer Brain Metastases: Barriers and Innovations.

Authors:  Jawad Fares; Deepak Kanojia; Aida Rashidi; Atique U Ahmed; Irina V Balyasnikova; Maciej S Lesniak
Journal:  Clin Breast Cancer       Date:  2019-06-05       Impact factor: 3.225

Review 2.  Brain metastases: neuroimaging.

Authors:  Whitney B Pope
Journal:  Handb Clin Neurol       Date:  2018

Review 3.  Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

Authors:  Koji Sakai; Kei Yamada
Journal:  Jpn J Radiol       Date:  2018-11-29       Impact factor: 2.374

4.  Progressive disease in glioblastoma: Benefits and limitations of semi-automated volumetry.

Authors:  Thomas Huber; Georgina Alber; Stefanie Bette; Johannes Kaesmacher; Tobias Boeckh-Behrens; Jens Gempt; Florian Ringel; Hanno M Specht; Bernhard Meyer; Claus Zimmer; Benedikt Wiestler; Jan S Kirschke
Journal:  PLoS One       Date:  2017-02-28       Impact factor: 3.240

Review 5.  Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

Authors:  Emilia Gryska; Justin Schneiderman; Isabella Björkman-Burtscher; Rolf A Heckemann
Journal:  BMJ Open       Date:  2021-01-29       Impact factor: 2.692

6.  Brain metastasis detection using machine learning: a systematic review and meta-analysis.

Authors:  Se Jin Cho; Leonard Sunwoo; Sung Hyun Baik; Yun Jung Bae; Byung Se Choi; Jae Hyoung Kim
Journal:  Neuro Oncol       Date:  2021-02-25       Impact factor: 12.300

  6 in total

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