16 hours ago Credit: The District

Astronomy and oncology do not make obvious bedfellows, but the search for new stars and galaxies has surprising similarities with the search for cancerous cells. This has led to new ways of speeding up image analysis in cancer research.

Despite their red-brick finish, the corridors of the Institute of Astronomy can seem more like an art gallery than a research centre, so beautiful are the images of supernovae and nebulae hanging there. Dr Nic Walton passes these every day as he makes his way to his office to study the formation of the Milky Way and search for planets outside our solar system.

On the screen of Walton's computer is what appears to be a map of stars in our Milky Way. In fact, it is something that is around 25 orders of magnitude smaller (that's ten followed by 25 zeros).

It is an image of cells taken from a biopsy of a patient with breast cancer; the 'stars' are the cells' nuclei, stained to indicate the presence of key proteins. It is the similarities between these patterns and those of astronomical images that he, together with colleagues at the Cancer Research UK (CRUK) Cambridge Institute, is exploiting in PathGrid, an interdisciplinary initiative to help automate the analysis of biopsy tissue.

"Both astronomy and cell biology deal with huge numbers: our Milky Way contains several billion stars, our bodies tens of trillions of cells," explained Walton.

PathGrid began at a cross-disciplinary meeting in Cambridge to discuss data management. Walton has been involved for many years with major international collaborations that, somewhat appropriately, amass an astronomical amount of data. But accessing data held by research teams across the globe was proving to be a challenge, with a lack of standardised protocols. Something needed to be done and Walton was part of an initiative to sort out this mess.

The issue of data management in an era of 'big data' is not unique to astronomy. Departments across the University from the Clinical School to the Library face similar issues and this meeting was intended to share ideas and approaches. It was at this meeting that Walton met James Brenton from the CRUK Cambridge Institute. They soon realised that data management was just one area where they could learn from each other: image analysis was another.

Walton and his colleagues in Astronomy capture their images using optical or near-infrared telescopes, such as the prosaically named Very Large Telescope or the recently launched Gaia satellite, the biggest camera in space with a billion pixels. These images must then be manipulated to adjust for factors including the telescope's own 'signature', cosmic rays and background illumination. They are tagged with coordinates to identify their location, and their brightness is determined.

Analysing these maps is an immense, but essential, task. Poring over images of tens of thousands of stars is a laborious, time-consuming process, prone to user error, so this is where computer algorithms come in handy. Walton and colleagues run their images through object detection software, which looks for astronomical features and automatically classifies them.

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Astronomy image analysis algorithms adapted to cancer screening method