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Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model
المؤلفون المشاركون
Crookes, Danny
Zhou, H.
Zhang, Kun
Zhang, Hongbin
Li, Ling
Shao, Yeqin
Liu, Dong
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-02-03
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging.
Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images.
Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements.
In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation.
To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit.
To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint.
We compare our method qualitatively and quantitatively with several standard segmentation models.
Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods.
By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Kun& Zhang, Hongbin& Zhou, H.& Crookes, Danny& Li, Ling& Shao, Yeqin…[et al.]. 2019. Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129599
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Kun…[et al.]. Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1129599
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Kun& Zhang, Hongbin& Zhou, H.& Crookes, Danny& Li, Ling& Shao, Yeqin…[et al.]. Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1129599
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129599
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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