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Centre for Advanced Research Computing


ARC is UCL's research, innovation and service centre for the tools, practices and systems that enable computational science and digital scholarship


Archive for December, 2023

Role models, outreach and changing the future of STE(A)M

By Samantha Ahern, on 20 December 2023

Promotion image for a Computing for Year 8/9 Girls event that took place on 13th December 2013

STE(A)M is for everyone, so why isn’t that reflected in the demography of STE(A)M professionals?

Sadly, when we look at the prevailing media representation and social narrative around STE(A)M it’s not surprising. Which, is why, events like the one I recently supported at Samuel Whitbred Academy organised by STEMPoint are incredibly important.

Over the course of the day I supported girls from four different secondary schools undertaking two computing challenges, one focused on coding, the other on robotics. In addition to the challenges, the participants also had the opportunity to hear from the STEM Ambassadors, most of them women, supporting the event about our careers, why and how we got into STEM.

This was a key part of the event as it enabled them to learn more about the different types of STE(A)M careers available, pathways into those careers and the diversity of the people working in them.

It is often said that you can’t be, what you can’t see. You also can’t be what you don’t know about. This is why since 2015 I have been a STEM Ambassador.

During this time I have supported a number of events requesting support via the STEM Learning platform, featured in a STEMettes Christmas Calendar, been profiled for BCS Women and designed and delivered some RI Masterclasses. On a more subtle level I use my full first name on publications and when public speaking, re-emphasising that I am a woman in STEM.

A combined image showing the hardware for the micro:bit coding challenge, banners for STEMPoint and VEX Robotics and one of the VEX robots in action.

The event at Samuel Whitbred Academy was a lot of fun, I always enjoy seeing students engaging with a STEM challenge. Especially seeing young women grow in confidence in their abilities and seeing computing as a space for them.

Unsurprisingly I spent most of the day supporting the robotics challenge. It was fantastic to see them rise to the challenge, and in some cases go beyond the extension task. I hope to support further events in 2024.

I strongly recommend becoming a STEM Ambassador to my colleagues, it really does make a big difference to the participants.

Randomising Blender scene properties for semi-automated data generation

By Ruaridh Gollifer, on 12 December 2023

Blender is a free and open-source software for 3D geometry rendering. Uses include modelling, simulation, animation, virtual reality applications, and more recently synthetic datasets generation. This last application is of particular interest in the field of medical imaging, where often there is limited real data that can be used to train machine learning models. By creating large amounts of synthetic but realistic data, we can improve the performance of models in tasks such as polyp detection in image guided surgery. Synthetic data generation has other advantages since using tools like Blender gives us more control and we can generate a variety of ground truth data from segmentation masks to optic flow fields, which in real data would be very challenging to generate or would involve extensive time consuming manual labelling. Another advantage of this approach is that often we can easily scale up our synthetic datasets by randomising parameters of the modelled 3D geometry. There can be challenges to make the data realistic and representative of the real data. 

The Problem 

The aim was to develop an add-on that would help researchers and medical imaging experts determine which range of parameter values make realistic synthetic images. Prior to the project, the dataset generation involved a more laborious process of manually creating scenes in Blender with parameters changed manually for introducing variation in the datasets. A more efficient process was needed during the prototyping of synthetic dataset generation to decide what range of parameters make sense visually, and therefore in the future, to more easily extend to other use cases.

What we did 

In collaboration with the UCL Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), research software engineers from ARC have developed a Blender add-on to randomise relevant parameters for the generation of datasets for polyp detection within the colon. The add-on was originally developed to render a highly diverse and (near) photo-realistic synthetic dataset of laparoscopic surgery camera views. To replicate the different camera positions used in surgery as well as the shape and appearance of the tissues, we focused on randomising three main components of the scene: camera transforms (camera orientation and location), geometry and materials. However, we allowed for more flexibility beyond these 3 main groups of parameters, implementing utilities to randomise other user-defined properties. The software also allows the following features: 1) setting the minimum and maximum bounds through an input file, 2) setting a randomisation seed for reproducibility, 3) exporting output parameters for a chosen number of frames to an output file. The add-on includes testing through Pytest, documentation for users and developers, example input and output files and a sample Blender scene.

The outcomes 

Version 1.0.0 of the Blender Randomiser is available under a BSD 3-Clause License. The GitHub repo is public where the software can be downloaded and installed with instructions provided on how to use the add-on. Examples of what can be produced in Blender can be found at the UCL Research Data Repository (N.B. these examples were produced manually prior to completion of this project).

Developer notes are also available to allow contributions. 


Sofia Minano and Ruaridh Gollifer

k-Plan now available to researchers!

By Sam Cunliffe, on 11 December 2023

One of ARC’s longest-running collaborations is with the Biomedical Ultrasound Group. Over the past three years, we’ve been developing a graphical user interface to simulate ultrasound treatment plans!

The k-Plan Logo

This software is called k-Plan, and licences are now available for sale through UCL’s commercial partner, BrainBox (who also sell ultrasound transducers).

Screenshot of the k-Plan GUI

If you’re interested in medical ultrasound, and think this software might help you: you can read the full UCL press release, or you can see some more snapshots of k-Plan in action.

The people behind the work…

Our collaboration is managed and led by Bradley Treeby. As well as me, there’s a full roster of research software engineers who’ve worked hard at various times over the last three years to make this happen:

  • Panayiotis Georgiou, ex-UCL now ARM.
  • Timothy Spain, ex-UCL now NERSC, 🇳🇴.
  • Ilektra Christidi, ARC, UCL.
  • Alessandro Felder, ARC, UCL.
  • Orod Razeghi, ex-UCL now University of Cambridge.
  • Idil Ozdemir, ARC, UCL.
  • Connor Aird, ARC, UCL.

We also have collaborators from the Brno University of Technology who work behind the scenes on the middleware and back-end of k-Plan and run the planning simulations in the cloud.