Research on Segmentation of Brain Tumor in MRI Image Based on Convolutional Neural Network.
Brain tumors are the brain diseases with the highest mortality and prevalence, and magnetic resonance imaging has high-resolution and multiparameter. As the basis for realizing the quantitative analysis of brain tumors, automatic segmentation plays a vital role in diagnosis and treatment. A new netw...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/5/2022
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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=158380402&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158380402 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/5/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158380402 158380402 158380402 10.1155/2022/7911801 158380402 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Research on Segmentation of Brain Tumor in MRI Image Based on Convolutional Neural Network. aug: au: Feng, Yurong Li, Jiao Zhang, Xi affil: Department of Radiology, The First People's Hospital of Jingmen City, Hubei, China sug: subj: Brain Neoplasms Physiopathology Brain Radiography Magnetic Resonance Imaging Methods Neural Networks (Computer) Neoplasm Grading Neoplasm Staging Models, Statistical Human Disease Attributes Tumor Burden Brain Pathology Brain Neoplasms Diagnosis Brain Neoplasms Therapy Sensitivity and Specificity ab: Brain tumors are the brain diseases with the highest mortality and prevalence, and magnetic resonance imaging has high-resolution and multiparameter. As the basis for realizing the quantitative analysis of brain tumors, automatic segmentation plays a vital role in diagnosis and treatment. A new network model is proposed to improve the accuracy of convolutional neural network segmentation of brain tumor regions and control the parameter space scale of the network model. The model first uses a convolutional layer composed of a series of 3D convolution filters to construct a backbone network for feature learning of input 3D MRI image blocks. Then, a pyramid structure constructed by a 3D convolutional layer is designed to extract and fuse features of tumor lesions and context information of different scales and then classify the fused feature at the voxel level to obtain segmentation results. Finally, a conditional random field is used to postprocess segmentation results for structured refinement. By designing massive ablation experiments to analyze the sensitivity of the essential modules of the comparison network, the results confirm that our method can better solve the problems faced by the traditional fully connected convolutional neural network. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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