Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large a...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 4; pp. 449 - 460 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2017
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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=124395647&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124395647 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2017 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124395647 124395647 124395647 10.1007/s10278-017-9983-4 124395647 ppf: 449 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. aug: au: Akkus, Zeynettin Galimzianova, Alfiia Hoogi, Assaf Rubin, Daniel Erickson, Bradley affil: Radiology Informatics Lab , Mayo Clinic , 200 First Street SW Rochester 55905 USA sug: subj: Magnetic Resonance Imaging Brain Radiography Artificial Intelligence Image Processing, Computer Assisted Algorithms Neural Networks (Computer) ab: Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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