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• W4225688082 endingPage "2445" @default.
• W4225688082 startingPage "2435" @default.
• W4225688082 abstract "Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue's disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients' data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing." @default.
• W4225688082 created "2022-05-05" @default.
• W4225688082 creator A5017154552 @default.
• W4225688082 creator A5028701730 @default.
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• W4225688082 date "2022-03-31" @default.
• W4225688082 modified "2023-10-16" @default.
• W4225688082 title "Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net" @default.
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• W4225688082 doi "https://doi.org/10.1007/s13042-022-01536-4" @default.
• W4225688082 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35378734" @default.
• W4225688082 hasPublicationYear "2022" @default.
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