Advances in Medical 3D Vision: Voxels and Beyond

Dear Colleagues,

Three-dimensional medical imaging, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) imaging, play a crucial role in healthcare, serving as "first-class citizens" in medical decision-making. These imaging modalities also include high-level shape and topological representations, which are critical for understanding anatomical structures and disease processes. In recent years, the analysis of medical 3D data has significantly benefited from advances in artificial intelligence, creating a field known as "Medical 3D Vision". This Special Issue will explore cutting-edge methods and applications of medical 3D data analysis, ranging from traditional voxel-based techniques for standard 3D medical images to approaches leveraging other geometric representations, such as point clouds, meshes, graphs, and implicit functions. By bringing together innovative research, we will bridge technical advancements and clinical applications, fostering a deeper understanding of how AI-driven 3D analysis can revolutionize diagnostics, intervention planning, and patient outcomes.

We invite original research and review articles that advance the field of Medical 3D Vision, focusing on both foundational methods and real-world applications. Specific topics of interest include, but are not limited to, the following:

• Three-dimensional medical image analysis;
• Implicit neural representations for 3D medical imaging;
• Multi-view reconstruction for dynamic imaging (e.g., cardiac or respiratory motion);
• Topological and geometric analysis in 3D medical data;
• Augmented reality (AR) and virtual reality (VR) applications in 3D medical imaging;
• Multi-scale representations for medical 3D imaging (e.g., hybrid voxel and graph-based methods);
• Explainable AI methods for 3D medical imaging;
• Advanced segmentation for complex 3D anatomical structures (e.g., vessels);
• Deep generative models for 3D medical data reconstruction and generation;
• Spatial-temporal AI in 4D medical imaging;
• Foundation models for 3D medical image segmentation and generation.

Dr. Jiancheng Yang
Dr. Hongwei Li
Dr. Hieu Le
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• medical 3D vision
• artificial intelligence for health
• 3D medical image analysis
• voxel-based imaging
• point clouds
• mesh-based analysis
• graph neural networks
• implicit representations
• deep geometric learning
• interventional imaging
• healthcare applications
• digital twins