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U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR Images

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Computer Analysis of Images and Patterns (CAIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13052))

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Abstract

Segmentation of cardiac magnetic resonance images (cMRI) remains a challenging task in the field of scientific research due to its significance in the medical assessment of cardiovascular diseases. Ensuring accurate segmentation of the heart structures, mainly the left ventricle cavity, serves to extract important information and has a major impact on the quantitative analysis of the heart function which helps to conduct the proper diagnosis of doctors. The present paper introduces a simple and efficient U-shaped convolutional neural network aiming to accurately segment the LV from cMR images. We applied our architecture for Left Ventricle (LV) segmentation on cardiac MR images (cMRI), from the Automated Cardiac Diagnosis Challenge (ACDC). Obtained results are promising. This simple model based on CNN has significantly fewer parameters rendering it less demanding in terms of computation. Nevertheless, it has provided accurate segmentation. The tested method achieved LV Dice scores of 0.958 at end-systolic time (ES) and 0.979 at end-diastolic time (ED), which yields a mean Dice score of 0.968 on the ACDC dataset.

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Author information

Authors and Affiliations

  1. Faculty of Sciences Monastir, University of Monastir, Monastir, Tunisia

    Khouloud Boukhris

  2. Faculty of Medicine Monastir, Medical Imaging Technology Lab – LTIM-LR12ES06, University of Monastir, Monastir, Tunisia

    Khouloud Boukhris, Ramzi Mahmoudi, Asma Ben Abdallah & Mohamed Hédi Bedoui

  3. Gaspard-Monge Computer-Science Laboratory, Paris-Est University, Mixed Unit CNRS-UMLV-ESIEE UMR8049, BP99, ESIEE Paris City Descartes, 93162, Noisy Le Grand, France

    Ramzi Mahmoudi

  4. Radiology Service- UR12SP40 CHU Fattouma Bourguiba, 5019, Monastir, Tunisia

    Mabrouk AbdelAli & Badii Hmida

Authors
  1. Khouloud Boukhris
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  2. Ramzi Mahmoudi
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  3. Asma Ben Abdallah
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  4. Mabrouk AbdelAli
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  5. Badii Hmida
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  6. Mohamed Hédi Bedoui
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Editor information

Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Nicolas Tsapatsoulis

  2. University of Cyprus, Nicosia, Cyprus

    Andreas Panayides

  3. University of Cyprus, Nicosia, Cyprus

    Theo Theocharides

  4. Cyprus University of Technology, Limassol, Cyprus

    Andreas Lanitis

  5. University of Cyprus, Nicosia, Cyprus

    Constantinos Pattichis

  6. University of Salerno, Salerno, Italy

    Mario Vento

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Boukhris, K., Mahmoudi, R., Abdallah, A.B., AbdelAli, M., Hmida, B., Bedoui, M.H. (2021). U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR Images. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-89128-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89127-5

  • Online ISBN: 978-3-030-89128-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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