Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 17, 04 December 2023

Open Access | Article

Application of deep learning in medical imaging segmentation

Keke Huang * 1
1 HD school

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 17, 19-25
Published 04 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Keke Huang. Application of deep learning in medical imaging segmentation. TNS (2023) Vol. 17: 19-25. DOI: 10.54254/2753-8818/17/20240702.

Abstract

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.

Keywords

deep learning, medical imaging segmentation, deep neural networks

References

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3. Malhotra, P. et al. (2022) Deep Neural Networks for medical image segmentation, Journal of Healthcare Engineering. Available at: https://www.hindawi.com/journals/jhe/2022/9580991/ (Accessed: 09 October 2023).

4. “Mrinal Walia.” Oct 13, 2022, Roboflow Blog. https://blog.roboflow.com/difference-semantic-segmentation-instance-segmentation/

5. Aljabri, M. and AlGhamdi, M. (2022) ‘A review on the use of Deep Learning for medical images segmentation’, Neurocomputing, 506, pp. 311–335.

6. Z. Li, F. Liu, W. Yang, S. Peng and J. Zhou, Dec. 2022,”A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999-7019.

7. Shelhamer, Evan, Jonathan Long, and Trevor Darrell, 2017, “Fully convolutional networks for semantic segmentation.” IEEE Trans. Pattern Anal. Mach. Intell. 39.4: 640-651.

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Modern Medicine and Global Health
ISBN (Print)
978-1-83558-197-1
ISBN (Online)
978-1-83558-198-8
Published Date
04 December 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/17/20240702
Copyright
04 December 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated