title: Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis.
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.
authors.affiliation: Silesian University of Technology, Faculty of Biomedical Engineering, Zabrze, Poland.
authors.forename: Pawel
authors.initials: P
authors.lastname: Szwarc
authors.affiliation: Silesian University of Technology, Faculty of Biomedical Engineering, Zabrze, Poland. Electronic address: jacek.kawa@polsl.pl.
authors.forename: Jacek
authors.initials: J
authors.lastname: Kawa
authors.affiliation: Silesian University of Technology, Faculty of Biomedical Engineering, Zabrze, Poland.
authors.forename: Marcin
authors.initials: M
authors.lastname: Rudzki
authors.affiliation: Silesian University of Technology, Faculty of Biomedical Engineering, Zabrze, Poland.
authors.forename: Ewa
authors.initials: E
authors.lastname: Pietka
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000150:complications / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D009389:Neovascularization, Pathologic / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Brain tumour; Computer aided diagnosis; Image segmentation; Magnetic resonance imaging; Perfusion maps