An atlas of classifiers-a machine learning paradigm for brain MRI segmentation.
We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 9; pp. 1833 - 1850 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2021
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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=152044051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152044051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2021 vid: 59 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152044051 151604170 152044051 NLM34313921 10.1007/s11517-021-02414-x NLM34313921 152044051 ppf: 1833 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An atlas of classifiers-a machine learning paradigm for brain MRI segmentation. aug: au: Gordon, Shiri Kodner, Boris Goldfryd, Tal Sidorov, Michael Goldberger, Jacob Raviv, Tammy Riklin affil: The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel sug: subj: Brain Multiple Sclerosis Neuroradiography Magnetic Resonance Imaging ab: We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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