nach oben

Arabian Journal for Science and Engineering

Erschienen in:

08.01.2021 | Research Article-Computer Engineering and Computer Science

Right Ventricle Segmentation of Magnetic Resonance Image Using the Modified Convolutional Neural Network

verfasst von: Nagaraj V. Dharwadkar, Amruta K. Savvashe

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 4/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, the segmentation model is developed using the convolutional neural network for automatic segmentation of a right ventricle MRI image. The proposed model is trained end-to-end using an RVSC dataset that contains the right ventricle magnetic resonance images. The proposed model gives state-of-art achievement for dice metric and also for the Jaccard index. The proposed model achieves an optimal model performance of dice metric performance with 0.91 (0.10) for the training dataset and 0.88 (0.12) for the validation dataset.

Vorheriger Artikel CookingQA: Answering Questions and Recommending Recipes Based on Ingredients

Nächster Artikel Requirement Engineering Challenges: A Systematic Mapping Study on the Academic and the Industrial Perspective

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

World Health Organization: Cardiovascular disease. Retrieved from World Health Organization: https://www.who.int/cardiovascular_diseases/about_cvd/en (2019)

Vision X-ray group: Retrieved from the importance of medical imaging: https://www.xray.com.au/importance-of-medical-imaging/ (2013, November 15)

Magnetic Resonance Imaging: Retrieved from Main Line Health. https://www.mainlinehealth.org/conditions-and-treatments/treatments/magnetic-resonance-imaging (2019)

Shankar, V.; Kumar, V.; Devagade, U., et al.: Heart disease prediction using CNN algorithm. SN Comput. Sci. 1, 170 (2020). https://doi.org/10.1007/s42979-020-0097-6CrossRef

Cois, A.; Galeotti, J.; Tamburo, R.; Sacks, M.; Stetten, G.: Shells and spheres: an n-dimensional framework for medial-based image segmentation. Int. J. Biomed. Imaging 2010, 980872 (2010). https://doi.org/10.1155/2010/980872CrossRef

Xie, L., et al.: Multi-atlas label fusion with augmented atlases for fast and accurate segmentation of cardiac MR images. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE (2015)

Guo, Z.-Z., et al.: Local Motion Intensity Clustering (LMIC) model for segmentation of right ventricle in cardiac MRI images. IEEE J. Biomed. Health Inform. 23(2), 723–730 (2018)CrossRef

Chang, Y., et al.: Automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE (2018)

Gupta, A., et al.: Cardiac MR image segmentation using deformable models. In: Computers in Cardiology 1993, Proceedings. IEEE (1993).

10.

Krasnopevtsev, P., et al.: A statistical approach to automatic heart segmentation and modelling from multiple modalities. In: 21st IEEE International Symposium on Computer-Based Medical Systems, 2008. CBMS'08. IEEE (2008).

11.

Constantinides, C., et al.: Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models. MIDAS J. Cardiac MR Left Ventricle Segment. Challenge (2009)

12.

Casta, C., et al. Evaluation of the dynamic deformable elastic template model for the segmentation of the heart in MRI sequences. MIDAS J. Cardiac MR Left Ventricle Segment. Challenge (2009)

13.

Santiago, C.; Nascimento, J.C.; Marques, J.S.: Segmentation of the left ventricle in cardiac MRI using a probabilistic data association active shape model. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE (2015)

14.

Yang, X.L., et al.: Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks. In: Computing in Cardiology Conference (CinC), 2016. IEEE (2016)

15.

Ronneberger, O.; Fischer, P.; Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, Cham (2015)

16.

univ-rouen.fr., caroline.petitjean at. litis. 2012. 2019. http://www.litislab.fr/?projet=1rvsc

17.

Zou, K.H., et al.: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad. Radiol. 11(2), 178–189 (2004)CrossRef

18.

Shi, R.; Ngan, K.N.; Li, S.: Jaccard index compensation for object segmentation evaluation. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE (2014)

19.

Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short axis MRI (2016)

20.

Avendi, M.R.; Kheradvar, A.; Jafarkhani, H.: Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn. Reson. Med. 78(6), 2439–2448 (2017). https://doi.org/10.1002/mrm.26631CrossRef

21.

Petitjean, C.; Zuluaga, M.A.; Bai, W.; Dacher, J.N.; Grosgeorge, D.; Caudron, J., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015). https://doi.org/10.1016/j.media.2014.10.004CrossRef

22.

Oktay, O.et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384–395 (2018)

23.

Chitiboi, T., et al.: Context-based segmentation and analysis of multi-cycle real-time cardiac MRI. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE (2014)

Titel: Right Ventricle Segmentation of Magnetic Resonance Image Using the Modified Convolutional Neural Network
verfasst von: Nagaraj V. Dharwadkar
Amruta K. Savvashe
Publikationsdatum: 08.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 4/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-05309-5

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 4/2021

Providing a Personalization Model Based on Fuzzy Topic Modeling

An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization

Design and Analysis of Pattern Matching Algorithms Based on QuRAM Processing

Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms

Index Term Selection Heuristics for Arabic Text Retrieval

Input–Output Modeling and Multi-objective Optimization of Weld Attributes in EBW

Premium Partner

Marktübersichten