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Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images

Authors:

Wei GuoAuthors Info & Claims

ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

Pages 27 - 30

https://doi.org/10.1145/3364836.3364842

Published: 24 August 2019 Publication History

Abstract

Pancreas segmentation in computed tomography (CT) is a challenging task because of the high inter-patient anatomical variability in both shape and size between patients. In this work, we proposed a convolutional neural networks based level set framework that can automatically segment a 3D images with the whole pancreas. Convolutional neural networks is applied to obtain an initial level set contour and then level set model is used to produce accurate segmentation results. Our method was compared with the state-of-the-art methods and evaluated on 20 CT images. The experiment results show that our approach achieves the highest dice scores than other methods.

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Cited By

Zhang CAchuthan AHimel G(2024)State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its VariantsIEEE Access10.1109/ACCESS.2024.339259512(78726-78742)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3392595
Bohlender SOksuz IMukhopadhyay A(2023)A Survey on Shape-Constraint Deep Learning for Medical Image SegmentationIEEE Reviews in Biomedical Engineering10.1109/RBME.2021.313634316(225-240)Online publication date: 2023
https://doi.org/10.1109/RBME.2021.3136343
Gong ZGuo CGuo WZhao DTan WZhou WZhang G(2021)A hybrid approach based on deep learning and level set formulation for liver segmentation in CT imagesJournal of Applied Clinical Medical Physics10.1002/acm2.1348223:1Online publication date: 6-Dec-2021
https://doi.org/10.1002/acm2.13482

Index Terms

Convolutional Neural Networks Based Level Set Framework for Pancreas Segmentation from CT Images
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine

August 2019

370 pages

ISBN:9781450372626

DOI:10.1145/3364836

Copyright © 2019 ACM.

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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Published: 24 August 2019

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ISICDM 2019

ISICDM 2019: The Third International Symposium on Image Computing and Digital Medicine

August 24 - 26, 2019

Xi'an, China

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Cited By

Zhang CAchuthan AHimel G(2024)State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its VariantsIEEE Access10.1109/ACCESS.2024.339259512(78726-78742)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3392595
Bohlender SOksuz IMukhopadhyay A(2023)A Survey on Shape-Constraint Deep Learning for Medical Image SegmentationIEEE Reviews in Biomedical Engineering10.1109/RBME.2021.313634316(225-240)Online publication date: 2023
https://doi.org/10.1109/RBME.2021.3136343
Gong ZGuo CGuo WZhao DTan WZhou WZhang G(2021)A hybrid approach based on deep learning and level set formulation for liver segmentation in CT imagesJournal of Applied Clinical Medical Physics10.1002/acm2.1348223:1Online publication date: 6-Dec-2021
https://doi.org/10.1002/acm2.13482

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