Version 1
: Received: 15 November 2023 / Approved: 16 November 2023 / Online: 16 November 2023 (14:31:38 CET)
How to cite:
Rudnicka, Z.; Szczepanski, J.; Pregowska, A. Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation. Preprints2023, 2023111108. https://doi.org/10.20944/preprints202311.1108.v1
Rudnicka, Z.; Szczepanski, J.; Pregowska, A. Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation. Preprints 2023, 2023111108. https://doi.org/10.20944/preprints202311.1108.v1
Rudnicka, Z.; Szczepanski, J.; Pregowska, A. Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation. Preprints2023, 2023111108. https://doi.org/10.20944/preprints202311.1108.v1
APA Style
Rudnicka, Z., Szczepanski, J., & Pregowska, A. (2023). Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation. Preprints. https://doi.org/10.20944/preprints202311.1108.v1
Chicago/Turabian Style
Rudnicka, Z., Janusz Szczepanski and Agnieszka Pregowska. 2023 "Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation" Preprints. https://doi.org/10.20944/preprints202311.1108.v1
Abstract
Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.
Keywords
Artificial Intelligence; Extended Reality; medical image scan segmentation.
Subject
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.
