An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.
Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and te...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 5; pp. 738 - 748 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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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=131880804&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131880804 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2018 vid: 31 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131880804 131880804 131880804 10.1007/s10278-018-0062-2 131880804 ppf: 738 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation. aug: au: Hoseini, Farnaz Bayat, Peyman Shahbahrami, Asadollah affil: Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran sug: subj: Neural Networks (Computer) Learning Methods Magnetic Resonance Imaging Methods Digital Imaging Methods Brain Neoplasms Diagnosis Human Algorithms Data Analysis, Statistical Minimum Data Set Task Performance and Analysis Sensitivity and Specificity ab: Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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