Issue link: https://maltatoday.uberflip.com/i/1440009
maltatoday | SUNDAY • 2 JANUARY 2022 6 CULTURE THE ASEMI (Automated segmentation of microtomography imaging) project, led by Professor Johann Briffa, Head of the Department of Communications & Computer Engineering at the Uni- versity of Malta, developed a tool us- ing artificial intelligence techniques to automatically segment volumetric mi- crotomography images, with particular application to Egyptian mummies. The project was in partnership with the Eu- ropean Synchrotron Radiation Facility (ESRF) in Grenoble, France, which has developed unique expertise in the appli- cation of synchrotron imaging to pale- ontology and archaeology. Microtomography is an X-ray imaging technique based on the same principle as the medical scanner, using synchrotron radiation instead of a conventional X-ray source to capture volumetric scans at much higher resolution and quality. This technology is able to provide 3D images to visualise the structure of ma- terials in a non-invasive and non-de- structive way, with applications in cul- tural heritage, materials research, life sciences, biomedical research and pale- ontology. One of the most recent applications is in Egyptology, through the investigation of animal mummies. In this application, trained specialists manually segment the volume into the various component ma- terials, such as textiles, organic tissues, balm resin, ceramics, and bones. Depending on the complexity and size of the dataset, this process is very time consuming, typically taking sever- al weeks for a small animal mummy. With the construction of the new beamline BM18, the same process is expected to be applied to human mummies, which would take consid- erably longer. The main result of the ASEMI pro- ject is the development of a tool to automatically perform this laborious process. Using the developed soft- ware, the human specialist only needs to manually segment a small sample of the volumetric image. This is used to train and automat- ically optimise a machine learning system, which can then segment the whole volume in a fraction of the time previously required. The accuracy obtained by the ASE- MI segmenter approaches the results of off-the-shelf commercial software using deep learning, at a much lower complexity. Following the principles of "Open Innovation, Open Science, Open to the World", the developed algorithms, data sets, and results have been made freely available to the gen- eral public. This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222. The full academic paper is published on PLOS ONE. Links to the developed software and the scanned volumes can be found in the paper. Egyptian mummies coming to life