12:30 - 13:00 |
Seeing is not believing - the case for automating image analysis |
Dave Barry, Francis Crick Institute |
You design your experiment, prepare your samples, acquire your images on a microscope and then transfer all your data to some storage device - now what? How do you go about converting all those images into a graph that succinctly communicates the result of your experiment? In this talk, you will learn why manual inspection of your images (even with the use of "analysis" software) is generally a bad idea - aside from being tedious and time-consuming (and often impractical for very large data sets), it introduces (unconscious) bias into your analysis. You will learn what image analysis is, what it can (and, more importantly, what it cannot) do and how you can go about taking advantage of it (spoiler alert: it's easier to get started than you think). |
||
13:00 - 14:00 |
Counting objects using a Jupyter Notebook |
Stefania Marcotti, King's College London |
13:00 - 14:00 |
Counting objects using FIJI |
Dale Moulding, University College London |
13:00 - 14:00 |
Counting objects using napari |
Martin Jones, Francis Crick Institute |
12:30 - 12:45 |
An Open-Source Framework for Automated High-Throughput Cell Biology ExperimentsThe implementation of deep-learning algorithms has become widespread in biology but the main drawback to their use is the requirement of large, high-quality datasets that are reproducible. It can be difficult to produce this manually and commercial automated systems that could solve this issue are expensive and inaccessible to many.Here, we present an open-source hardware and low-cost framework that allows for the automatic high-throughput generation of large amounts of cell biology data. This framework has been adapted to incorporate long-term differentiation experiments in pluripotent Ntera-2 cells, single-cell manipulation to form neural circuits, and high-throughput Ca2+ imaging. |
H. J. McCourty, B. M. Sanches-Tafolla, J. Zhou, A. V. Nikolaev – Sheffield University
|
12:45 - 13:00 |
EnderScopeThe EnderScope is a scanning microscope based on an Ender 3D printer, developed to detect and analyse microplastics in seawater samples. The 3D printer provides a convenient, inexpensive motorised stage enabling the microscope to scan a large area. It can image in bright field or fluorescence – and the system can easily convert back into a printer using a quick release clamp. |
N. Burke, M. Pickering – University College Dublin
|
13:00 - 13:15 |
UC2UC2 is a modular optics toolbox that can implement everything from an afocal telescope to a light sheet imaging system, using modular cubes that click together. Using the latest embedded electronics board, smartphone technology, and other consumer electronics technology, UC2 demonstrates that a wide variety of imaging experiments can be demonstrated in a highly accessible way. There is a strong emphasis on its use in educational settings, allowing students to start with the basic principles of Fourier optics and build up to fully functioning microscopes. |
B. Diederich – Institut für Photonische Technologien, Jena
|
13:15 - 13:30 |
OpenFlexureThe OpenFlexure Project includes designs for several geometries of microscope, using 3D printed flexures for precise motion control. This is combined with an optics package optimised for small-format webcam sensors to create an automated microscope that can scan slides and operate effectively with high magnification objectives. |
J. Knapper, F. Whiteford, R. Bowman – University of Glasgow |
13:30 - 13:45 |
Make it Visible: from 3D data to tactile model and the practicalities of designing for outreachMany studies talk about the idea that their data could be 3D printed and then used for outreach, perhaps with visually impaired participants. How many of these projects actually move from the theoretical to the practical and what are the barriers to participation? This research considers the practical aspects of implementing a 3D data to 3D print process, the ethical barriers that make finding an audience to work with and to test your ideas difficult, and the question how other tools can provide the users with the autonomy to explore your datasets unassisted?
|
A. Ball – Natural History Museum |