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Titel:

Segmentation of Left Ventricle in Short-Axis MR Images Based on Fully Convolutional Network and Active Contour Model.

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Tran, Tien Thanh; Tran, Thi-Thao; Ninh, Quoc Cuong; Bui, Minh Duc; Pham, Van-Truong:
Seitenangaben Beitrag:
49–59
Kapitel Beitrag:
Artificial Intelligence and Cyber Systems
Abstract:
Abstract: Left ventricle (LV) segmentation from cardiac MRI images plays an important role in clinical diagnosis of the LV function. In this study, we proposed a new approach for left ventricle segmentation based on deep neural network and active contour model (ACM). The paper proposed a coarse-to-fine segmentation framework. In the first step of the framework, the fully convolutional network was employed to achieve coarse segmentation of LV from input cardiac MR images. Especially, instead of u...     »
Stichworte:
Left ventricle segmentation; Active contour model; Fully convolutional network; Deep learning
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Herausgeber:
Yo-Ping Huang; Wen-June Wang; Hoang An Quoc; Le Hieu Giang; Nguyen-Le Hung
Kongress- / Buchtitel:
Computational Intelligence Methods for Green Technology and Sustainable Development. Proceedings of the 5th International Conference on GTSD2020. 27-28 November 2020, Ho Chi Minh City, Vietnam
Datum der Konferenz:
27-28 November 2020
Verlag / Institution:
Springer Nature Switzerland AG
Verlagsort:
Cham
Publikationsdatum:
28.10.2020
Jahr:
2021
Seiten:
655
Nachgewiesen in:
Scopus
Print-ISBN:
978-3-030-62323-4
E-ISBN:
978-3-030-62324-1
Serientitel:
Part of the Advances in Intelligent Systems and Computing book series (AISC)
Serienbandnummer:
1284
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:https://doi.org/10.1007/978-3-030-62324-1_5
WWW:
https://doi.org/10.1007/978-3-030-62324-1
Copyright Informationen:
Open Access. Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Format:
Text
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