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  • Biomica platform : At the cutting edge of medical imaging

    Biomica platform : At the cutting edge of medical imaging

    Among the research platforms at Télécom SudParis, BioMICA has developed bio-imaging applications that have already been approved by the medical field. Airways, its 3D representation software, received funding from Télécom & Société Numérique Carnot Institute

    One of the recommendations included in the March 2017 France AI Strategy report was to put artificial intelligence to work to improve medical diagnosis. The BioMICA research platform (which stands for Bio-Medical Imaging & Clinical Applications) has made this goal its mission.

    “We aim to develop tools that can be used in the clinical setting,” says Catalin Fetita, professor at Télécom SudParis and director of the bio-medical imaging platform. “Our applied research focuses on computer-aided diagnosis involving medical and biological imaging,” he explains. As a specialist in image analysis and processing, Catalin Fetita offers the platform true expertise in the area of medical imaging, particularly in lung imaging.

    AirWays, or another way of seeing lungs

    AirWays is “image marker” software (like biomarkers in biology). Based on a sequence of lung images taken by a scanner, it extracts as much information as possible for clinicians to assist them in their diagnosis by offering a range of different visualization and classification options. “The quantitative aspect is very important, we do not only want to offer better visual quality,” Catalin Fetita explains. “We offer the possibility of obtaining improved measurements of morphological differences in several areas of the respiratory system at different moments in time. This help clinicians decide which treatment to choose.” In terms of quantified results, the software can detect 95% of stenosis cases, which is the narrowing of bronchial tubes that affects respiratory capacity.

    AirWays software uses a graphic grid representation of bronchial tube surfaces after analyzing clinical images and then generates 3D images to view them both “inside and outside” (above, a view of the local bronchial diameter using color coding). This technique allows doctors to plan more effectively for endoscopies and operations that were previously performed by sight.

    “For now, we have limited ourselves to the diagnosis-analysis aspect, but I would also like to develop a predictive aspect,” says the researcher. This perspective is what motivated Carnot TSN to help finance AirWays in December 2017. “This new budget will help us improve and optimize the software’s interface and increase its computing power to make it a true black box for automatic and synthetic processing,” explains Catalin Fetita, who also hopes to work towards commercializing the software.

    A platform for medicine of the future

    In addition to its many computer workstations for developing its medical software, the BioMICA platform features two laboratories for biological experimentation. One of the laboratories has a containment level of L1 (any biological agent that is non-pathogenic for humans) and the other is L2 (possible pathogen with low risk). Both will help advance the clinical studies in cellular bio-imaging.

    In addition, Catalin Fetita and his team are preparing a virtual reality viewing station to provide a different perspective of the lung tissue analyzed by Airways. “Our platform works thanks to research partnerships and technological transfers,” he explains, “but we can also use it to provide services for clinical studies.”

  • Data-Mobility or the art of modeling travel patterns

    Data-Mobility or the art of modeling travel patterns

    The French have a saying that reflects the daily routine of millions of Parisians: “métro-boulot-dodo” (metro-work-sleep).  While this seems to be the universal experience for Il-de-France residents, individual variations exist. Some individuals only use public transport via one of the two major networks, RATP or SNCF, but others prefer driving. There are also those who change from the metro to the RER train, or leave their car part way and take a train. All of this information can be found through mobile data analysis. Vincent Gauthier, associate research professor at Télécom SudParis, has become a specialist in the area.

    Using mobile networks to understand mobility

    Determining someone’s travel itinerary based on the mobile data provided by their operator is not an easy task. “A telephone only transmits its GPS position to applications that request it, such as Waze,” Vincent Gauthier explains. “The only knowledge an operator can use to establish a person’s geographic location is which mobile base stations they were connected to during their travels.” The French telephone network, which is shared between different operators including Orange, SFR and Bouygues, forms an irregular grid pattern (see Fig. 3). The different relay or base stations provide a network connection based on clearly defined zones. When a person leaves a zone, they automatically enter another one, and their telephone connects to the new corresponding base station. The size of these zones varies in each region. In the Ile-de-France region, a large number of base stations are concentrated and clustered together in Paris, but there are much fewer in the Seine-et-Marne region.

    Using mobile networks to understand mobility

    Fig. 1 : Method used to aggregate the transport networks to closely analyze the route taken.

    Fig. 2 : Origin-destination matrix for a day in the Ile-de-France region.

    Fig. 3 : Grid pattern for the mobile network base stations.

    Data-Mobility or the art of modeling travel patterns

    The information produced from these connections only allows origin-destination matrices that are more or less detailed to be established. As an expert in the graphical representation of large volumes of data (Fig. 2), Vincent Gauthier wants to take this analysis a step further: “How does a person travel? Why? Where does the person live? How many other people take the same route? Answering these questions could help us optimize mobility options.”

    To reproduce the exact route an individual takes based on this non-specific information, he has worked on a new method with another researcher from Télécom SudParis, Mounim El Yacoubi (ARMEDIA team–EPH department).

    From optimizing transportation to geodemographics

    “Mounim and I have patented a method for automatically processing routes, which allows us to determine what types of transport a person has taken during their journey,” Vincent Gauthier explains. Thanks to their “method for route estimation using mobile data”, the two researchers can superimpose the different transport networks over the information the operators receive from the base stations (Fig. 1). “To identify the most likely road or rail journey the users have taken based on their route, we must use a huge database including the locations of the base stations, train stations and the maps of the different transport networks.” They are currently working with Bouygues to develop route estimations in “near real time”.

    In their work, the two researchers are drawing on previous socio-demographic studies they conducted in Milan and in Africa. “We participated in estimating population density in the Ivory Coast and Senegal,” explains Vincent Gauthier. “The goal was to provide socio-demographic data that was lacking in these countries, so that the United Nations could establish more reliable statistics.”

    Vincent Gauthier’s work goes beyond simply modeling big data; his expertise leads us to rethink the geography of our regions: “By analyzing individuals’ routes and optimizing transport options accordingly, we could possibly divide the Ile-de-France region into more relevant sub-areas.”

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