14 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…
Anuradha Kar, Manuel Petit, Yassin Refahi, Guillaume Cerutt + 2 more
Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly…
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…
Tomas Vicar, Jiri Chmelik, Roman Jakubicek, Larisa Chmelikova + 4 more
In this paper, U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is…
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…
Adrian Wolny, Lorenzo Cerrone, Athul Vijayan, Rachele Tofanelli + 13 more
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge.…
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…
Elisa Pedone, Irene de Cesare, Criseida G. Zamora-Chimal, David Haener + 8 more
Advances in microscopy, microfluidics and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah – a…
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…
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…