Automated segmentation of cell organelles in volume electron microscopy using deep learning.

Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets.

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile.

Biodegradable, Self-Reinforcing Vascular Grafts for In Situ Tissue Engineering Approaches.

Clinically available small-diameter synthetic vascular grafts (SDVGs) have unsatisfactory patency rates due to impaired graft healing. Therefore, autologous implants are still the gold standard for small vessel replacement. Bioresorbable SDVGs may be an alternative, but many polymers have inadequate biomechanical properties that lead to graft failure.

Corrigendum: Mapping the functional anatomy and topography of the cardiac autonomic innervation for selective cardiac neuromodulation using MicroCT.

[This corrects the article DOI: 10.3389/fcell.2022.