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

Issue title: Interdisciplinary Nature of Information Processing Special Issue Dedicated to Giancarlo Mauri on the Occasion of His 70th Birthday

Guest editors: Alberto Dennunzio, Gheorghe Păun, Grzegorz Rozenberg and Claudio Zandron

Article type: Research Article

Authors: Rundo, Leonardo^{a; *} | Militello, Carmelo^b | Vitabile, Salvatore^c | Russo, Giorgio^b | Sala, Evis^{d; *} | Gilardi, Maria Carla^{e; C}

Affiliations: [a] Department of Radiology, University of Cambridge, Cambridge, UK | [b] Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy | [c] Department of Biomedicine Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy | [d] Department of Radiology, University of Cambridge, Cambridge, UK | [e] Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy. [email protected]

Correspondence: [C] Address for corresponcence: Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Contrada Pietra Pollastra-Pisciotto, 90015 Cefalù (PA), Italy

Note: [*] Also afiliated: Cancer Research UK Cambridge Centre, Cambridge, UK.

Abstract: 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 natureinspired 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.

Keywords: Nature-inspired computing, artificial intelligence, medical image analysis, biomedical data integration

DOI: 10.3233/FI-2020-1887

Journal: Fundamenta Informaticae, vol. 171, no. 1-4, pp. 345-365, 2020