1. You are here:
  2. Homepage
  3. Meetings & Features
  4. Learning Zone
  5. Open-source Software and Hardware Workshops

Open-source Software and Hardware Workshops

Tuesday 4th July, 12:30 – 14:00 in Learning Zone Lecture Theatre

Open-source Software

Representatives from DAIM will be delivering an introduction to image analysis workshop in the Learning Zone @ MMC 2023 from 12:30 - 14:00 on July 4th. We'll commence with a brief introduction to the topic of image analysis and why it's necessary to ensure reproducible results are derived from microscopy data. We'll then split into parallel demonstrations to show how you can get started performing simple tasks like counting objects using three popular platforms for image analysis: FIJI, napari and Jupyter notebooks.
What Will I Learn?
The demonstrations will illustrate a simple workflow, consisting of a number of simple steps, designed to accomplish the goal of counting objects in a microscopy image. The steps involved will vary slightly depending on the platform used, but will resemble the following:
  1. Opening images
  2. Using filters to suppress noise
  3. Segmenting images using grey level thresholding
  4. Counting objects
  5. Basic morphological quantification
Schedule
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

Wednesday 5th July, 12:30 – 14:00 in Learning Zone Lecture Theatre

Microscopy Maker Faire

Sharing designs for experiments and apparatus has always been part of the scientific process, and this is increasingly done using “open hardware” licenses that guarantee the freedom to use, study, modify, and redistribute a piece of hardware. Open hardware microscopes range from simple DIY designs to cutting-edge projects costing hundreds of thousands of pounds, and promise to improve access to, and repeatability of, microscopy experiments. This is particularly true when automation is needed, often enabled by accessible electronics like Raspberry Pi and Arduino. This year at MMC2023 we will host a session in the Learning Zone showcasing five Open Hardware projects in microscopy.

 

Schedule
12:30 - 12:45
An Open-Source Framework for Automated High-Throughput Cell Biology Experiments
The 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
EnderScope
The 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
UC2
UC2 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
OpenFlexure
The 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 outreach
Many 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?

I'll be presenting 3D-printed models of microscopic specimens, alongside a smart speaker system called "Museum in a Box". These models allow people with visual impairments to explore microscopic natural history samples autonomously.
A. Ball – Natural History Museum