https://scholars.lib.ntu.edu.tw/handle/123456789/457161
Title: | Brain tumor segmentation using U-net and edge contour enhancement | Authors: | Ho, T.-W. Qi, H. Lai, F. FU-REN XIAO FEI-PEI LAI JIN-MING WU |
Keywords: | Brain tumor; Edge contour enhancement; Image segmentation; Quantitative analysis | Issue Date: | 2019 | Publisher: | Association for Computing Machinery | Journal Volume: | Part F147955 | Start page/Pages: | 75-79 | Source: | ACM International Conference Proceeding Series | Abstract: | Segmentation of brain tumors by magnetic resonance imaging (MRI) plays a pivotal role in evaluating the disease condition and deciding on a future treatment plan. This type of segmentation task usually requires extensive experience from medical practitioners and enormous amounts of time. To mitigate these issues, this study deploys a segmentation model for brain tumors based on U-Net and a comprehensive data processing approach, including target magnification and image transformation, such as data augmentation and edge contour enhancement. Compared with the manual segmentation of radiologists, which is considered the gold standard, the proposed model revealed good performance and yielded a median dice similarity coefficient of 0.637 (interquartile range: 0.382-0.803) for brain tumor segmentation. Results with and without edge contour enhancement demonstrated significant differences based on the Wilcoxon signed-tank test with P = 0.028. The proposed model enables effective segmentation of brain tumors determined by MRI and can assist medical practitioners tasked with analyzing complicated medical images. © 2019 Association for Computing Machinery. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066012688&doi=10.1145%2f3316551.3316554&partnerID=40&md5=819d90a9dfc7b07b979541d352d79beb https://scholars.lib.ntu.edu.tw/handle/123456789/457161 |
DOI: | 10.1145/3316551.3316554 | SDG/Keyword: | Brain; Chemical analysis; Data handling; Digital signal processing; Image analysis; Image enhancement; Magnetic resonance imaging; Medical imaging; Metadata; Tumors; Brain tumor segmentation; Brain tumors; Edge contour; Image transformations; Inter quartile ranges; Medical practitioner; Segmentation models; Similarity coefficients; Image segmentation |
Appears in Collections: | 醫學系 |
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