Implicit Neural Representation in Medical Imaging: A Comparative Survey

IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 2373–2383

Abstract

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

Authors 6

1. Amirali Molaei

   Iran University of Science and Technology

   Affiliation as printed

   Iran University of Science and Technology,School of Computer Engineering,Iran

   School of Computer Engineering, Iran University of Science and Technology, Iran

2. Amirhossein Aminimehr

   Iran University of Science and Technology

   Affiliation as printed

   Iran University of Science and Technology,School of Computer Engineering,Iran

   School of Computer Engineering, Iran University of Science and Technology, Iran

3. Amirhossein Kazerouni

   Iran University of Science and Technology

   Affiliation as printed

   Iran University of Science and Technology,School of Electrical Engineering,Iran

   School of Electrical Engineering, Iran University of Science and Technology, Iran

4. Bobby Azad

   South Dakota State University

   Affiliation as printed

   South Dakota State University,Department of Electrical Engineering and Computer Science,USA

   Department of Electrical Engineering and Computer Science, South Dakota State University, USA

5. Reza Azad Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Faculty of Electrical Engineering and Information Technology,Germany

   Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany

6. Dorit Merhof

   Fraunhofer Institute for Digital Medicine · University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany

   Faculty of Informatics and Data Science, University of Regensburg, Germany

   Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany