State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.
Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping ba...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 6; pp. 1443 - 1465 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
2020
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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=147529122&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147529122 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: 2020 vid: 33 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147529122 144558409 147529122 147529122 10.1007/s10278-020-00367-5 147529122 ppf: 1443 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms. aug: au: Fatima, Anam Shahid, Ahmad Raza Raza, Basit Madni, Tahir Mustafa Janjua, Uzair Iqbal affil: Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), 45550, Islamabad, Pakistan sug: subj: Machine Learning Deep Learning Brain Anatomy and Histology Image Processing, Computer Assisted Methods Magnetic Resonance Imaging Algorithms Technology, Medical Skull Anatomy and Histology Brain Radiography Brain Mapping Methods ab: Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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