Progress in Biomedical Engineering 

About: Progress in Biomedical Engineering is an academic journal published by IOP Publishing. The journal publishes majorly in the area(s): Physics & Computer science. It has an ISSN identifier of 2516-1091. Over the lifetime, 98 publications have been published receiving 857 citations.

Vascularization in tissue engineering: fundamentals and state-of-art

Abstract: Vascularization is among the top challenges that impede the clinical application of engineered tissues. This challenge has spurred tremendous research endeavor, defined as vascular tissue engineering (VTE) in this article, to establish a pre-existing vascular network inside the tissue engineered graft prior to implantation. Ideally, the engineered vasculature can be integrated into the host vasculature via anastomosis to supply nutrient to all cells instantaneously after surgery. Moreover, sufficient vascularization is of great significance in regenerative medicine from many other perspectives. Due to the critical role of vascularization in successful tissue engineering, we aim to provide an up-to-date overview of the fundamentals and VTE strategies in this article, including angiogenic cells, biomaterial/bio-scaffold design and bio-fabrication approaches, along with the reported utility of vascularized tissue complex in regenerative medicine. We will also share our opinion on the future perspective of this field.

Deep learning in medical image registration

Abstract: Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.