An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI.
Glioma is an aggressive type of cancer that develops in the brain or spinal cord. Due to many differences in its shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding cancerous tissues is a challenging task. In recent researches, the combina...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 6; pp. 1634 - 1648 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2022
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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=160503236&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160503236 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2022 vid: 35 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160503236 158650364 160503236 160503236 10.1007/s10278-022-00655-2 160503236 ppf: 1634 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI. aug: au: Barzegar, Zeynab Jamzad, Mansour affil: Sharif University of Technology, Tehran, Iran sug: subj: Brain Neoplasms Pathology Glioma Pathology Brain Neoplasms Diagnosis Glioma Diagnosis Brain Radiography Magnetic Resonance Imaging Methods Cervical Atlas Machine Learning Minimum Data Set Technology, Medical Neoplasm Grading Neoplasm Staging Human Models, Statistical ab: Glioma is an aggressive type of cancer that develops in the brain or spinal cord. Due to many differences in its shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding cancerous tissues is a challenging task. In recent researches, the combination of multi-atlas segmentation and machine learning methods provides robust and accurate results by learning from annotated atlas datasets. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training phase of learning methods, we proposed a semi-supervised unified framework for multi-label segmentation that formulates this problem in terms of a Markov Random Field energy optimization on a parametric graph. To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low- and high-grade glioma tumors. Experimental results indicate competitive performance compared to the state-of-the-art methods. Compared with the top ranked methods, the proposed framework obtains the best dice score for segmenting of "whole tumor" (WT), "tumor core" (TC) and "enhancing active tumor" (ET) regions. The achieved accuracy is 94 % characterized by the mean dice score. The motivation of using MRF graph is to map the segmentation problem to an optimization model in a graphical environment. Therefore, by defining perfect graph structure and optimum constraints and flows in the continuous max-flow model, the segmentation is performed precisely. 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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