Massimo Martinelli

Thesaurus project: design of new autonomous underwater vehicles for documentation and protection of underwater archaeological sites

The Thesaurus Project, funded by the Regione Toscana, combines humanistic and technological research aiming at developing a new generation of cooperating Autonomous Underwater Vehicles and at documenting ancient and modern Tuscany shipwrecks. Technological research will allow performing an archaeological exploration mission through the use of a swarm of autonomous, smart and self-organizing underwater vehicles. Using acoustic communications, these vehicles will be able to exchange each other data related to the state of the exploration and then to adapt their behavior to improve the survey. The archival research and archaeological survey aim at collecting all reports related to the underwater evidences and the events of sinking occurred in the sea of Tuscany. The collected data will be organized in a specific database suitably modeled.

Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

Cell image analysis ontology

Cell image analysis in microscopy is the core activity of cytology and cytopathology for assessing cell physiological (cellular structure and function) and pathological properties. Biologists usually make evaluations by visually and qualitatively inspecting microscopic images: this way, they are particularly able to recognize deviations from normality. Nevertheless, automated analysis is strongly preferable for obtaining objective, quantitative, detailed, and reproducible measurements, i.e., features, of cells. Yet, the organization and standardization of the wide domain of features used in cytometry is still a matter of challenging research. In this paper, we present the Cell Image Analysis Ontology (CIAO), which we are developing for structuring the cell image features domain. CIAO is a structured ontology that relates different cell parts or whole cells, microscopic images, and cytometric features. Such an ontology has incalculable value since it could be used for standardizing cell image analysis terminology and features definition. It could also be suitably integrated into the development of tools for supporting biologists and clinicians in their analysis processes and for implementing automated diagnostic systems. Thus, we also present a tool developed for using CIAO in the diagnosis of hematopoietic diseases.

Mirror Mirror on the Wall... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

A persons well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

Remote Sensing for Maritime Monitoring and Vessel Prompt Identification

The main purpose of the work described in this paper concerns the development of a platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade implementation of several image processing algorithms that take as input Radar or Optical maps captured by satellite-borne sensors. More in detail, the processing chain is dedicated to (i) the detection of vessel targets in the input map, (ii) the refined estimation of the vessel most descriptive geometrical features and, finally, (iii) the estimation of the kinematic status of the vessel. This platform will represent a new tool for combating unauthorized fishing, irregular migration and related smuggling activities.

Deep Learning Approach to Human Osteosarcoma Cell Detection and Classification.

The early diagnosis of a cancer type is a fundamental goal in cancer treatment, as it can facilitate the subsequent clinical management of patients. The leading importance of classifying cancer patients into high or low risk groups has led many research teams, both from biomedical and bioinformatics field, to study the application of Deep Learning (DL) methods. The ability of DL tools to detect key features from complex datasets is a fundamental achievement in early diagnosis and cell cancer progression. In this paper, we apply DL approach to classification of osteosarcoma cells. Osteosarcoma is the most common bone cancer occurring prevalently in children or young adults. Glass slides of different cell populations were cultured from Mesenchimal Stromal Cells (MSCs) and differentiated in healthy bone cells (osteoblasts) or osteosarcoma cells. Images of such samples are recorded with an optical microscope. DL is then applied to identify and classify single cells. The results show a classification accuracy of 0.97. The next step is the application of our DL approach to tissue in order to improve digital histopathology.