Version 1
: Received: 25 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (17:10:52 CEST)
How to cite:
Krikid, F.; Rositi, H.; Vacavant, A. State-of-the-Art of Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues. Preprints2024, 2024092030. https://doi.org/10.20944/preprints202409.2030.v1
Krikid, F.; Rositi, H.; Vacavant, A. State-of-the-Art of Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues. Preprints 2024, 2024092030. https://doi.org/10.20944/preprints202409.2030.v1
Krikid, F.; Rositi, H.; Vacavant, A. State-of-the-Art of Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues. Preprints2024, 2024092030. https://doi.org/10.20944/preprints202409.2030.v1
APA Style
Krikid, F., Rositi, H., & Vacavant, A. (2024). State-of-the-Art of Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues. Preprints. https://doi.org/10.20944/preprints202409.2030.v1
Chicago/Turabian Style
Krikid, F., Hugo Rositi and Antoine Vacavant. 2024 "State-of-the-Art of Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues" Preprints. https://doi.org/10.20944/preprints202409.2030.v1
Abstract
Microscopic image segmentation (MIS) plays a pivotal role in various fields such as medical imaging and biology. With the advent of deep learning (DL), numerous methods have emerged for automating and improving the accuracy of this crucial image analysis task. This systematic literature review (SLR) aims to provide an exhaustive overview of the state-of-the-art DL methods employed for the segmentation of microscopic images. In this review, we analyze a diverse array of studies published in the last five years, highlighting their contributions, methodologies, datasets, and performance evaluations. We explore the evolution of DL techniques and their adaptation to specific segmentation challenges, from cell and nucleus segmentation to tissue analysis. This paper, through the integration of existing knowledge, provides valuable perspectives for researchers involved in the field of microscopic image segmentation.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
