Systematic Review of Techniques in Brain Image Synthesis using Deep Learning

• URL: http://arxiv.org/abs/2309.04511v1
• Date: Fri, 8 Sep 2023 14:20:01 GMT
• Title: Systematic Review of Techniques in Brain Image Synthesis using Deep Learning
• Authors: Shubham Singh, Ammar Ranapurwala, Mrunal Bewoor, Sheetal Patil, Satyam Rai
• Abstract summary: The need for medical image synthesis to improve diagnostic accuracy and decrease invasiveness in medical procedures is emphasized. The paper examines various methods and techniques for brain image synthesis, including 2D to 3D constructions, MRI synthesis, and the use of transformers. The review concludes by exploring the future potential of this field and the opportunities for further advancements in medical imaging using deep learning techniques.
• Score: 3.1998815025767033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This review paper delves into the present state of medical imaging, with a specific focus on the use of deep learning techniques for brain image synthesis. The need for medical image synthesis to improve diagnostic accuracy and decrease invasiveness in medical procedures is emphasized, along with the role of deep learning in enabling these advancements. The paper examines various methods and techniques for brain image synthesis, including 2D to 3D constructions, MRI synthesis, and the use of transformers. It also addresses limitations and challenges faced in these methods, such as obtaining well-curated training data and addressing brain ultrasound issues. The review concludes by exploring the future potential of this field and the opportunities for further advancements in medical imaging using deep learning techniques. The significance of transformers and their potential to revolutionize the medical imaging field is highlighted. Additionally, the paper discusses the potential solutions to the shortcomings and limitations faced in this field. The review provides researchers with an updated reference on the present state of the field and aims to inspire further research and bridge the gap between the present state of medical imaging and the future possibilities offered by deep learning techniques.

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