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Deep Interactive Segmentation of Uncertain Regions with Shadowed Sets

Authors:

Chao MaAuthors Info & Claims

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

Pages 244 - 248

https://doi.org/10.1145/3364836.3364885

Published: 24 August 2019 Publication History

Abstract

Pancreas segmentation is a challenging task in medical image analysis because of its large variations in texture, location, shape and size and the high similarity to the surrounding tissues especially around the boundary regions, which leads to the high segmentation uncertainty and makes the results inaccurate. Existing fully automatic segmentation methods rarely achieve sufficiently accurate and robust results. To tackle this problem, we propose a deep learning based interactive uncertain segmentation method which can involve the domain knowledge in the process of segmentation in an interactive and iterative way. Specially, the proposed method describes the uncertain regions of pancreatic CT images based on shadowed sets theory which are further corrected through interaction. The proposed method is evaluated on a challenging 3D pancreatic CT images dataset collected from the Changhai Hospital. The experimental results demonstrate that our proposed method outperforms the existing methods in terms of both the Dice similarity coefficient of 78% and the pixel-wise accuracy of 96%, which reveals the effectiveness and the potential of our method in clinical settings.

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Cited By

Kim SPark CJeon GKim SKim J(2025)Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AIBioengineering10.3390/bioengineering1201008112:1(81)Online publication date: 16-Jan-2025
https://doi.org/10.3390/bioengineering12010081
Marinov ZJäger PEgger JKleesiek JStiefelhagen R(2024)Deep Interactive Segmentation of Medical Images: A Systematic Review and TaxonomyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.345262946:12(10998-11018)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3452629
Luo ZHu JZhang QWang G(2023)Induction of interval shadowed sets from the perspective of maintaining fuzzinessInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.11.019153:C(219-238)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.ijar.2022.11.019
Show More Cited By

Index Terms

Deep Interactive Segmentation of Uncertain Regions with Shadowed Sets
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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Published In

cover image ACM Other conferences

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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Xidian University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2019

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Refereed limited

Funding Sources

National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Shanghai Innovation Action Project of Science and Technology

Conference

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

Kim SPark CJeon GKim SKim J(2025)Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AIBioengineering10.3390/bioengineering1201008112:1(81)Online publication date: 16-Jan-2025
https://doi.org/10.3390/bioengineering12010081
Marinov ZJäger PEgger JKleesiek JStiefelhagen R(2024)Deep Interactive Segmentation of Medical Images: A Systematic Review and TaxonomyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.345262946:12(10998-11018)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3452629
Luo ZHu JZhang QWang G(2023)Induction of interval shadowed sets from the perspective of maintaining fuzzinessInternational Journal of Approximate Reasoning10.1016/j.ijar.2022.11.019153:C(219-238)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.ijar.2022.11.019
Zaman FZhang LZhang HSonka MWu X(2023)Segmentation quality assessment by automated detection of erroneous surface regions in medical imagesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107324164:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107324
Leiser FRank SSchmidt-Kraepelin MThiebes SSunyaev A(2023)Medical informed machine learningArtificial Intelligence in Medicine10.1016/j.artmed.2023.102676145:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.artmed.2023.102676
Ramadan HLachqar CTairi H(2020)A survey of recent interactive image segmentation methodsComputational Visual Media10.1007/s41095-020-0177-5Online publication date: 22-Aug-2020
https://doi.org/10.1007/s41095-020-0177-5

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