Article

Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network

Authors:

Renzhen Wang,

Shilei Cao,

Kai Ma,

Deyu Meng,

Yefeng ZhengAuthors Info & Claims

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

Pages 157 - 165

https://doi.org/10.1007/978-3-030-32226-7_18

Published: 13 October 2019 Publication History

Abstract

Semantic segmentation has been popularly addressed using fully convolutional networks (FCNs) with impressive results if the training set is diverse and large enough. However, FCNs often fail to achieve satisfactory results due to a limited number of manually labelled samples in medical imaging. In this paper, we propose a conjugate fully convolutional network (CFCN) to address this challenging problem. CFCN is a novel framework where pairwise samples are input and synergistically segmented in the network for capturing a rich context representation. To avoid overfitting introduced by appearance and shape changes in a small number of training samples, a fusion module is designed to provide proxy supervision for the network training process. Quantitative evaluation shows that the proposed method has a significant performance improvement on pathological liver segmentation.

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Cited By

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Al-Kababji ABensaali FDakua SHimeur Y(2023)Automated liver tissues delineation techniquesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105532117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105532
Raju ACheng CHuo YCai JHuang JXiao JLu LLiao CHarrison A(2020)Co-heterogeneous and Adaptive Segmentation from Multi-source and Multi-phase CT Imaging Data: A Study on Pathological Liver and Lesion SegmentationComputer Vision – ECCV 202010.1007/978-3-030-58592-1_27(448-465)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58592-1_27

Index Terms

Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
        Video segmentation
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information

Published In

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI

Oct 2019

894 pages

ISBN:978-3-030-32225-0

DOI:10.1007/978-3-030-32226-7

Editors:
Dinggang Shen
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
,
Tianming Liu
University of Georgia, Athens, GA, USA
,
Terry M. Peters
Western University, London, ON, Canada
,
Lawrence H. Staib
Yale University, New Haven, CT, USA
,
Caroline Essert
University of Strasbourg, Illkirch, France
,
Sean Zhou
United Imaging Intelligence, Shanghai, China
,
Pew-Thian Yap
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
,
Ali Khan
Western University, London, ON, Canada

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 October 2019

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Al-Kababji ABensaali FDakua SHimeur Y(2023)Automated liver tissues delineation techniquesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105532117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105532
Raju ACheng CHuo YCai JHuang JXiao JLu LLiao CHarrison A(2020)Co-heterogeneous and Adaptive Segmentation from Multi-source and Multi-phase CT Imaging Data: A Study on Pathological Liver and Lesion SegmentationComputer Vision – ECCV 202010.1007/978-3-030-58592-1_27(448-465)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58592-1_27

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Cited By

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Semi- and Weakly- Supervised Semantic Segmentation with Deep Convolutional Neural Networks

Joint weakly and fully supervised learning for surface defect segmentation from images

Multi-scale deep context convolutional neural networks for semantic segmentation

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