Biomedical Image Reconstruction: A Survey

TL;DR

This survey reviews the evolution of biomedical image reconstruction from traditional methods to modern deep learning techniques, highlighting recent research trends and the role of the MLMIR workshop in advancing the field.

Contribution

It provides a comprehensive overview of biomedical image reconstruction techniques and summarizes current research trends based on MLMIR publications, aiding machine learning researchers in understanding the field.

Findings

Deep learning has significantly shifted biomedical image reconstruction paradigms.

Traditional analytical and iterative methods are increasingly complemented or replaced by deep learning approaches.

The MLMIR workshop has played a key role in fostering research and discussion in this field.

Abstract

Biomedical image reconstruction research has been developed for more than five decades, giving rise to various techniques such as central and filtered back projection. With the rise of deep learning technology, biomedical image reconstruction field has undergone a massive paradigm shift from analytical and iterative methods to deep learning methods To drive scientific discussion on advanced deep learning techniques for biomedical image reconstruction, a workshop focusing on deep biomedical image reconstruction, MLMIR, is introduced and is being held yearly since 2018. This survey paper is aimed to provide basic knowledge in biomedical image reconstruction and the current research trend in biomedical image reconstruction based on the publications in MLMIR. This survey paper is intended for machine learning researchers to grasp a general understanding of the biomedical image reconstruction field and the current research trend in deep biomedical image reconstruction.