Pooled CRISPR screening has emerged as a powerful method of mapping gene functions
thanks to its scalability, affordability, and robustness against well or plate-specific …

Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge.
Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations …

The objective of this paper is to study the impact of limited datasets on deep learning
techniques and conventional methods in semantic image segmentation and to conduct a …

This article contributes to a more adequate modelling of emotions encoded in speech, by
addressing four fallacies prevalent in traditional affective computing: First, studies …

This paper investigates the impact of the amount of training data and the shape variability on
the segmentation provided by the deep learning architecture U-Net. Further, the correctness …

In this work, an unsupervised volumetric semantic instance segmentation of the plasma
membrane of HeLa cells as observed with serial block face scanning electron microscopy is …

Methane (CH4) hydrate dissociation and CH4 release are potential geohazards currently
investigated using X-ray computed tomography (XCT). Image segmentation is an important …

Computational cell segmentation is a vital area of research, particularly in the analysis of
images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is …

Electron microscopy (EM) enables imaging at a resolution of nanometers and can shed light
on how cancer evolves to develop resistance to therapy. Acquiring these images has …

Transmission electron microscopy has been long considered to be the gold standard for the
visualization of cellular ultrastructure. However, analysis is often limited to two dimensions …