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Microscopy preprints – Bioimage analysis tools

Posted by , on 11 March 2022

Here is a curated selection of preprints published recently. In this post, we focus specifically on new bioimage analysis tools only.

Cloud-enabled Fiji for reproducible, integrated and modular image processing. Ling-Hong Hung, Evan Straw, Shishir Reddy, Robert Schmitz, Zachary Colburn, Ka Yee Yeung

Figure extracted from Hung et al.

A pipeline to track unlabeled cells in wide migration chambers using pseudofluorescence. Antonello Paola, Marcus Thelen, Rolf Krause, Pizzagalli Diego Ulisse

Figure extracted from Paola et al.

TissUUmaps 3: Interactive visualization and quality assessment of large-scale spatial omics data. Nicolas Pielawski, Axel Andersson, Christophe Avenel, Andrea Behanova, Eduard Chelebian, Anna Klemm, Fredrik Nysjö, Leslie Solorzano, Carolina Wählby

Figure extracted from Pielawski et al.

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. Clarence Yapp, Edward Novikov, Won-Dong Jang, Tuulia Vallius, Yu-An Chen, Marcelo Cicconet, Zoltan Maliga, Connor A. Jacobson, Donglai Wei, Sandro Santagata, Hanspeter Pfister, Peter K. Sorger

Figure extracted from Yapp et al.

Bespoke data augmentation and network construction enable image classification on small microscopy datasets. Ian Groves, Jacob Holmshaw, David Furley, Benjamin D. Evans, Marysia Placzek, Alexander G. Fletcher

Figure extracted from Groves et al.

A surface morphometrics toolkit to quantify organellar membrane ultrastructure using cryo-electron tomography. Benjamin A Barad, Michaela Medina, Daniel Fuentes, R Luke Wiseman, Danielle A Grotjahn

Figure extracted from Barad et al.

MemBrain: A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms. Lorenz Lamm, Ricardo D. Righetto, Wojciech Wietrzynski, Matthias Pöge, Antonio Martinez-Sanchez, Tingying Peng, Benjamin D. Engel

Figure extracted from Lamm et al.

Interpretable Unsupervised Diversity Denoising and Artefact Removal. Mangal Prakash, Mauricio Delbracio, Peyman Milanfar, Florian Jug

Figure extracted from Prakash et al.

Machine learning meets classical computer vision for accurate cell identification. Elham Karimi, Morteza Rezanejad, Benoit Fiset, Lucas Perus, Sheri A.C. McDowell, Azadeh Arabzadeh, Gaspard Beugnot, Peter Siegel, Marie-Christine Guiot, Daniela F. Quail, Kaleem Siddiqi, Logan A. Walsh

Figure extracted from Karimi et al.

Automated Analysis of Neuronal Morphology through an Unsupervised Classification Model of Neurites. Amin Zehtabian, Joachim Fuchs, Britta Eickholt, Helge Ewers

Figure extracted from Zehtabian et al.

AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images. Mohsin Bilal, Yee Wah Tsang, Mahmoud Ali, Simon Graham, Emily Hero, Noorul Wahab, Katherine Dodd, Harvir Sahota, Wenqi Lu, Mostafa Jahanifar, Andrew Robinson, Ayesha Azam, Ksenija Benes, Mohammed Nimir, Abhir Bhalerao, Hesham Eldaly, Shan E Ahmed Raza, Kishore Gopalakrishnan, Fayyaz Minhas, David Snead, Nasir Rajpoot

Figure extracted from Bilal et al.

DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis. Théo Aspert, Didier Hentsch, Gilles Charvin

Figure extracted from Aspert et al.

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets. Anuradha Kar, Manuel Petit, Yassin Refahi, Guillaume Cerutt, Christophe Godin, Jan Traas

Figure extracted from Kar et al.
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