15 papers · ranked by Valyu relevance
Ryan Conrad, Kedar Narayan
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train models on images that provide limited cellular contexts, precluding…
Tim Scherr, Johannes Seiffarth, Bastian Wollenhaupt, Oliver Neumann + 5 more
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell…
Liming Wu, Alain Chen, Paul Salama, Kenneth Dunn + 1 more
The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, researchers generally segment cells by their nuclei. While effective tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional…
Julian Hennies, José Miguel Serra Lleti, Constantin Pape, Sultan Bekbayev + 3 more
Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and…
Felix Y. Zhou, Clarence Yapp, Zhiguo Shang, Stephan Daetwyler + 14 more
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by…
Hayden Nunley, Binglun Shao, Prateek Grover, Jaspreet Singh + 10 more
For investigations into fate specification and cell rearrangements in live images of preimplantation embryos, automated and accurate 3D instance segmentation of nuclei is invaluable; however, the performance of segmentation methods is limited by the images’ low signal-to-noise ratio and high voxel anisotropy and the…
Yuxing Wang, Junhan Zhao, Hongye Xu, Cheng Han + 5 more
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform…
Peng Liu, Boyu Shen, Liyuan Liu, Qiong Wang + 5 more
We present MitoEM 2.0, a curated data resource for training and evaluating three-dimensional (3D) mitochondria instance segmentation in volume electron microscopy. The collection assembles multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) spanning diverse tissues and species, with expert-verified instance labels…
Vijay Venu Thiyagarajan, Arlo Sheridan, Kristen M. Harris, Uri Manor
Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across…
Christopher J Buswinka, Richard T. Osgood, Hidetomi Nitta, Artur A. Indzhykulian
Segmenting individual instances of mitochondria from imaging datasets can provide rich quantitative information, but is prohibitively time-consuming when done manually, prompting interest in the development of automated algorithms using deep neural networks. Existing solutions for various segmentation tasks are…
Matthias Arzt, Joran Deschamps, Christopher Schmied, Tobias Pietzsch + 3 more
We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. Labkit is specifically designed to work…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within…
Kamal L Nahas, João Ferreira Fernandes, Colin Crump, Stephen Graham + 1 more
Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that…
Tridib K. Biswas, Jonathan Vacher, Sophie Molholm, Pascal Mamassian + 1 more
The visual system operates by segmenting visual inputs into distinct perceptual objects. Segmentation is dynamic, as revealed by the tempo of perceptual choices and neural activity in visual cortex. Dynamics for natural stimuli however, are poorly understood because natural scene segmentation is ambiguous and…
Yichen He, Marco Camaiti, Lucy E. Roberts, James M. Mulqueeney + 2 more
The increased availability of 3D image data requires improving the efficiency of digital segmentation, currently relying on manual labelling, especially when separating structures into multiple components. Automated and semi-automated methods to streamline segmentation have been developed, such as deep learning and…