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Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends
Abstract
Semantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors present perspectives on the development of an architectural, and operational mechanism of each machine learning-based semantic segmentation approach with merits and demerits. In this regard, researchers have proposed different Semseg methods and examined their performance in a variety of applications such as medical image analysis (e.g., medical image classification and segmentation). A review of recent advances in Semseg techniques are presented in this paper by applying computational image processing and machine learning methods. This article is further presented a comprehensive investigation on how different architectures are helpful for medical image segmentation. Finally, advantages, open challenges, and possible future directions are elaborated in the discussion part, beneficial to the research community to understand the significance of the available medical imaging segmentation technology based on Semseg and thus deliver robust segmentation solutions.
Citations
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Web of Science
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Authors (8)
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Imran Qureshi
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Junhua Yan
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Qaisar Abbas
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Awais Bin Riaz
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Abdul Wahid
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Muhammad Waseem Jan Khan
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Information Fusion
no. 90,
pages 316 - 352,
ISSN: 1566-2535 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Qureshi I., Yan J., Abbas Q., Shaheed K., Riaz A. B., Wahid A., Khan M. W. J., Szczuko P.: Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends// Information Fusion -Vol. 90, (2022), s.316-352
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.inffus.2022.09.031
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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