Automated 2D Slice-Based Skull Stripping Multi-View Ensemble Model on NFBS and IBSR Datasets.
This study proposed and evaluated a two-dimensional (2D) slice-based multi-view U-Net (MVU-Net) architecture for skull stripping. The proposed model fused all three TI-weighted brain magnetic resonance imaging (MRI) views, i.e., axial, coronal, and sagittal. This 2D method performed equally well as...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 2; pp. 374 - 385 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155757674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155757674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2022 vid: 35 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155757674 154890855 155757674 155757674 10.1007/s10278-021-00560-0 155757674 ppf: 374 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated 2D Slice-Based Skull Stripping Multi-View Ensemble Model on NFBS and IBSR Datasets. aug: au: Fatima, Anam Madni, Tahir Mustafa Anwar, Fozia Janjua, Uzair Iqbal Sultana, Nasira affil: Medical Imaging and Diagnostic Lab, National Centre of Artificial Intelligence, Park Road, Tarlai Kalan, 45550, Islamabad, Pakistan sug: subj: Skull Radiography Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Brain Radiography Human Models, Theoretical Sensitivity and Specificity Deep Learning Biofeedback Brain Anatomy and Histology ab: This study proposed and evaluated a two-dimensional (2D) slice-based multi-view U-Net (MVU-Net) architecture for skull stripping. The proposed model fused all three TI-weighted brain magnetic resonance imaging (MRI) views, i.e., axial, coronal, and sagittal. This 2D method performed equally well as a three-dimensional (3D) model of skull stripping. while using fewer computational resources. The predictions of all three views were fused linearly, producing a final brain mask with better accuracy and efficiency. Meanwhile, two publicly available datasets—the Internet Brain Segmentation Repository (IBSR) and Neurofeedback Skull-stripped (NFBS) repository—were trained and tested. The MVU-Net, U-Net, and skip connection U-Net (SCU-Net) architectures were then compared. For the IBSR dataset, compared to U-Net and SC-UNet, the MVU-Net architecture attained better mean dice score coefficient (DSC), sensitivity, and specificity, at 0.9184, 0.9397, and 0.9908, respectively. Similarly, the MVU-Net architecture achieved better mean DSC, sensitivity, and specificity, at 0.9681, 0.9763, and 0.9954, respectively, than the U-Net and SC-UNet for the NFBS dataset. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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