Medical Image Semantic Segmentation Using Deep Learning: A Survey

DOI

10.2991/978-94-6463-496-9_25How to use a DOI?

Keywords

Medical Imaging; Semantic Segmentation; Deep Learning

Abstract

Biomedical image segmentation has witnessed a significant advancement with the emergence of deep learning (DL) technologies, which become pivotal in medical image analysis. This paper presents a comprehensive review of the evolution and current state of medical image segmentation (MIS) techniques, with a particular focus on semantic segmentation enabled by DL. We conduct a critical analysis of various neural network architectures, including the latest developments in vision transformers, and their impact on enhancing the accuracy and efficiency of medical image processing. We identify key advancements, discuss current challenges, and suggest potential future directions.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ferialle Lahreche
AU  - Abdelouahab Moussaoui
AU  - Slimane Oulad-Naoui
PY  - 2024
DA  - 2024/08/31
TI  - Medical Image Semantic Segmentation Using Deep Learning: A Survey
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 324
EP  - 345
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_25
DO  - 10.2991/978-94-6463-496-9_25
ID  - Lahreche2024
ER  -