A Survey on Nature-Inspired Medical Image Analysis : A Step Further in Biomedical Data Integration

• Autorzy: Rundo Leonardo , Militello Carmelo , Vitabile Salvatore , Russo Giorgio , Sala Evis , Gilardi Maria Carla

Identyfikatory

DOI

10.3233/FI-2020-1887

• Języki publikacji: EN

Abstrakty

EN

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets — i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring — to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image analysis methods is surveyed, aiming at establishing a common platform for beneficial exchanges among computer scientists and clinicians. In particular, this review focuses on the main nature-inspired computational techniques applied to medical image analysis tasks, namely: physical processes, bio-inspired mathematical models, Evolutionary Computation, Swarm Intelligence, and neural computation. These frameworks, tightly coupled with Clinical Decision Support Systems, can be suitably applied to every phase of the clinical workflow. We show that the proper combination of quantitative imaging and healthcare informatics enables an in-depth understanding of molecular processes that can guide towards personalised patient care.

Słowa kluczowe

EN

artificial intelligence; biomedical data integration; medical image analysis; nature-inspired computing

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2020
• Tom: Vol. 171, nr 1-4
• Strony: 345--365
• Opis fizyczny: Bibliogr. 134 poz., rys., wykr.

Twórcy

autor

Rundo Leonardo

• Department of Radiology, University of Cambridge, Cambridge, UK
• Cancer Research UK Cambridge Centre, Cambridge, UK

autor

Militello Carmelo

• Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy

autor

Vitabile Salvatore

• Department of Biomedicine Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy

autor

Russo Giorgio

• Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy

autor

Sala Evis

• Department of Radiology, University of Cambridge, Cambridge, UK
• Cancer Research UK Cambridge Centre, Cambridge, UK

autor

Gilardi Maria Carla

• Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy
• Cancer Research UK Cambridge Centre, Cambridge, UK.

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).