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Brain Tumor Segmentation Using U-Net and Edge Contour Enhancement

Published: 24 February 2019 Publication History

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.

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Cited By

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  • (2024)Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound ImagesBioengineering10.3390/bioengineering1102012811:2(128)Online publication date: 29-Jan-2024
  • (2022)Classification of Brain Tumor MRI Scans using Transfer Learning with a Comparative Analysis on Pre-Trained Networks2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9726062(1-7)Online publication date: 21-Jan-2022
  • (2020)Enhancing and Nonenhancing 3D Brain Tumor Segmentation with Modified Swish Activation and Double U-net ArchitectureProceedings of the 2020 4th International Conference on Vision, Image and Signal Processing10.1145/3448823.3448847(1-5)Online publication date: 9-Dec-2020
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    cover image ACM Other conferences
    ICDSP '19: Proceedings of the 2019 3rd International Conference on Digital Signal Processing
    February 2019
    170 pages
    ISBN:9781450362047
    DOI:10.1145/3316551
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 24 February 2019

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    Author Tags

    1. Image segmentation
    2. brain tumor
    3. edge contour enhancement
    4. quantitative analysis

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    ICDSP 2019
    ICDSP 2019: 2019 3rd International Conference on Digital Signal Processing
    February 24 - 26, 2019
    Jeju Island, Republic of Korea

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    Cited By

    View all
    • (2024)Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound ImagesBioengineering10.3390/bioengineering1102012811:2(128)Online publication date: 29-Jan-2024
    • (2022)Classification of Brain Tumor MRI Scans using Transfer Learning with a Comparative Analysis on Pre-Trained Networks2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9726062(1-7)Online publication date: 21-Jan-2022
    • (2020)Enhancing and Nonenhancing 3D Brain Tumor Segmentation with Modified Swish Activation and Double U-net ArchitectureProceedings of the 2020 4th International Conference on Vision, Image and Signal Processing10.1145/3448823.3448847(1-5)Online publication date: 9-Dec-2020
    • (2020)Fuse Box Image Segmentation Based On Improved FCMProceedings of the 2020 4th International Conference on Video and Image Processing10.1145/3447450.3447471(88-92)Online publication date: 25-Dec-2020

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