%0 Computer Program
%A Kievits, Arent J.
%A Duinkerken, B. H. Peter
%A Lane, Ryan
%A de Heus, Cecilia 
%A van Beijeren Bergen en Henegouwen, Daan 
%A Höppener, Tibbe 
%A H. G. Wolters, Anouk
%A Liv, Nalan
%A Giepmans, Ben N.G.
%A Hoogenboom, Jacob P.
%D 2024
%T Sample dataset and software for FAST-EM array tomography 
%U 
%R 10.4121/bf3f2b23-2328-4d81-a0f4-05fdb33117d7.v1
%K volume electron microscopy
%K FAST-EM
%K array tomography
%K image processing
%X <p>This repository contains a sample electron microscopy dataset produced with "FAST-EM array tomography" [1] and accompanying software to produce a 3D reconstruction of this dataset. FAST-EM array tomography is a technique for acquiring and reconstructing volume (3D) multibeam electron microscopy datasets. The sample dataset is part of a larger volume (3D) electron microscopy dataset included in the publication [Kievits et al. (2024)] (DOI will be added). This sample dataset is used for (automatic) testing of the software presented in the publication and as a demonstration example of 3D reconstruction.</p><p><br></p><p><strong>Datasets</strong></p><p><em>MCF-7 cells</em></p><p>3 serial sections of cultured Michigan Cancer Foundation-7 (MCF-7) cells (partial dataset). The dataset contains 25 images per section in a 5 x 5 grid, covering an area of 120 x 120 microns in each section of the sample. The `megafield_field_meta_data.yaml` contains metadata from the acquisition and is used in the processing.</p><p><br></p><p><em>Full dataset visualization</em></p><ul><li>[Partial dataset link](https://webknossos.tnw.tudelft.nl/links/BIduioDOrpiBh_ED)&nbsp;</li><li>[Full dataset link](https://webknossos.tnw.tudelft.nl/links/9TwKDPdjNa4JHIYt)</li></ul><p><br></p><p><em>Organization</em></p><p><br></p><p>┌ 20231107_MCF7_UAC_test (project)</p><p>├──┬ S001 (section --&gt; z)</p><p>│&nbsp;├── 000_000_0.tiff</p><p>│&nbsp;├── 000_001_0.tiff</p><p>│ ├── 000_002_0.tiff</p><p>│ │&nbsp;&nbsp;...</p><p>│ ├── 004_004_0.tiff</p><p>│ ├── mega_field_meta_data.yaml</p><p>├─── S002</p><p>└─── S003</p><p><br></p><p><strong>Software </strong></p><p><code>scripted-render-pipeline</code> : <a href="https://github.com/hoogenboom-group/scripted-render-pipeline" target="_blank">https://github.com/hoogenboom-group/scripted-render-pipeline</a></p><p>Automated pipeline for processing FAST-EM array tomography datasets</p><ul><li>Automated post-correction of images</li><li>Import to <code>render-ws</code></li><li>Export to (self-managed) <a href="https://webknossos.org/" target="_blank">WebKnossos</a> instances</li></ul><p><br></p><p><code>interactive-render-workflow</code>: <a href="https://github.com/hoogenboom-group/interactive-render-workflow" target="_blank">https://github.com/hoogenboom-group/interactive-render-workflow</a></p><p>Interactive workflow for aligning FAST-EM array tomography datasets</p><ul><li>Interactive post-correction of images</li><li>Import to <code>render-ws</code></li><li>Interactive 2D stitching</li><li>Interactive 3D alignment</li><li>Export to local WebKnossos instances</li></ul><p><br></p><p><code>fastem-sofima</code> : <a href="https://github.com/hoogenboom-group/fastem-sofima" target="_blank">https://github.com/hoogenboom-group/fastem-sofima</a></p><p>Scalable Optical Flow-based Image Montaging and Alignment (SOFIMA [2]) of FAST-EM array tomography datasets</p><p><br></p><p><strong>References</strong></p><p>[1] ref to paper (accepted in MiM, will be added once online)</p><p>[2] https://github.com/google-research/sofima</p><p><br></p><p><strong>License </strong></p><p>Data is published under a CC0 license. The software repositories are published under separate GPL-3.0 licenses.</p><p><br></p><p><br></p>
%I 4TU.ResearchData