Implicit Neural Representation in Medical Imaging: A Comparative Survey
Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof
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
\href{https://github.com/mindflow-institue/Awesome-Implicit-Neural-Representations-in-Medical-imaging}.
Finally, we aim to consistently incorporate the most recent and relevant papers
regularly.