Digital pathology is opening up new possibilities in modern medicine, and the era of optical microscopy may be coming to an end. Following joint efforts by computer experts and pathologists, new tools allow microscope slides to be digitised, annotated and classified using easily accessible platforms. With Project Cytomine, researchers at the University of Liège have stayed in the forefront of the development of biomedical tools. The advantages of the application involved are many. Slide information can be stored and shared, and displayed for teaching purposes. Above all, the programme performs part of the tedious work of analyzing biological cells and tissues, which is the daily grind of the pathologist. This software has possibilities that go beyond the university, and its continued development will be guaranteed through the launch of a spin-off in late 2014.

Since the 19^th century, the procedure for observing and analyzing biological cells or tissues has not changed at all. A sample is placed on a glass slide, which can then be placed under the microscope and examined by a pathologist. With the advent of digital pathology, this procedure has begun to be superseded. This constitutes a minor revolution in the world of medicine. Various innovations, including the Cytomine software developed by researchers at the Institut Montefiore and the GIGA Centre at the University of Liège, make it possible to digitize, classify and share the information contained on slides with high image resolution and analytic clarity that is equal or superior to the clarity of optical microscopes. “The final result is a little like the technology of Google Maps,” said Cytomine project scientific coordinator Raphaël Marée. “Except instead of geographical maps, [the programme] displays biological samples that represent tens of thousands of cells or tissues”.

Project Cytomine was begun in 2010. It is an Internet platform that can store and classify digitized microscope slides, thus offering a number of new possibilities for cytology and histology, among other things. In the age of digitization, the procedure is almost disturbingly simple. Platforms and data banks are everywhere on the Internet. In the biomedical realm, the practical aspect of the project is emphasized. “Such technology for analyzing images didn’t exist 15 years ago,” explains Benjamin Stévens, computer expert at the University of Liège and coordinator for the spinoff launch. “Even Google Maps had not been developed yet. The costs of digitization were prohibitive, and they’re only now coming down. In addition, the speed at which images were processed was slow; it took more than 20 minutes to digitize one image. Today it only takes 2 minutes. The quality of the digitized image back then was not equal to the image provided by a precision microscope, or else the size of the image was too large for the memory capacity we had then, and Internet connections were not as fast, and experts were reluctant to make the change. That was understandable, they were responsible for diagnoses they made, and they trusted the equipment they were used to using. They had to be convinced that the tool we were developing would really help them work”.

An intelligent algorithm…

But the new field of bioinformatics developed rapidly and soon won respect. In 2005 Raphaël Marée designed a generic model for the automatic recognition and classification of images as part of his doctoral research. There was nothing specifically medical about this development; the method could work with any images, and it was designed to classify them in terms of what they represented. This algorithm was much imitated. The technology improved around it, and when Raphaël Marée began to work with Benjamin Stévens, the programme was aimed at the biomedical field.   

In 2008 their research team was contacted by an American company working on products that would assist in the detection of cervical cancer. The company was working on automated systems for the preparation of samples. That is, they produced slides and digitized them at a very high level of resolution (as high as 100,000 x 100,000 pixels). These images could be viewed on a screen instead of through a microscope. But the samples might contain hundreds of thousands of healthy cells, with perhaps only a few betraying the presence of a (pre-) cancerous condition. At this point, digitization has not yet made the pathologist’s task easier. If the researcher wants to detect tumorous cells, they have to be individually identified. “What the company wanted us to do was to develop an algorithm for detecting abnormal cells,” recalls Raphaël Marée. “An application that could find a needle in a haystack.