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A Novel Hybrid Network for H&N Organs at Risk Segmentation

Published: 25 September 2020 Publication History

Abstract

In this paper, we find a network which can achieve better result than the state of the art for Head and Neck Organ at Risks (OARs) segmentation. At first, we enumerate the popular networks, and sum up their characteristic. We extract the main components from these popular networks and we design experiment to evaluate these components. We split the experiment into two stage. At the first stage experiment, we try to find out which components and constructions can let the network achieve better result than baseline model, Unet, for H&N OAR segmentation. After finding out the useful components and constructions, we try to mix them up to build a novel network which absorbs all their merits. At last, we get a hybrid network, Attention-W-net which gets the best result and defeat the state of the art. All networks are evaluated on 16th CSTRO conference H&N OAR segmentation competition dataset.

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  • (2024)Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentationBioMedical Engineering OnLine10.1186/s12938-024-01238-823:1Online publication date: 8-Jun-2024

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  1. A Novel Hybrid Network for H&N Organs at Risk Segmentation

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    ICBIP '20: Proceedings of the 5th International Conference on Biomedical Signal and Image Processing
    August 2020
    99 pages
    ISBN:9781450387767
    DOI:10.1145/3417519
    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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    • Sichuan University

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    Published: 25 September 2020

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    Author Tags

    1. Head and neck organ at risks
    2. attention-w-net
    3. convolution neural network
    4. medical image segmentation

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    Funding Sources

    • Natural?Science?Foundation?of?Guangdong?Province
    • Pearl River S&T Nova Program of Guangzhou

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    • (2024)Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentationBioMedical Engineering OnLine10.1186/s12938-024-01238-823:1Online publication date: 8-Jun-2024

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