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8 September 2022

Artificial Intelligence Applied to Medical Imaging and Computational Biology

,
and
1
Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
2
Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy
3
Institute of Molecular Bioimaging and Physiology, Italian National Research Council (IBFM-CNR), 90015 Cefalù, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci.2022, 12(18), 9052;https://doi.org/10.3390/app12189052
This article belongs to the Special Issue Artificial Intelligence Applied to Medical Imaging and Computational Biology
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The Special Issue “Artificial Intelligence Applied to Medical Imaging and Computational Biology” of the Applied Sciences Journal has been curated from February 2021 to May 2022, which covered the state-of-the-art and novel algorithms and applications of Artificial Intelligence methods for biomedical data analysis, ranging from classic Machine Learning to Deep Learning.
Medical imaging and computational biology continuously pose new fundamental medical and biological questions that often give rise to novel challenges in Artificial Intelligence. Moreover, the amount of biomedical data is constantly increasing due to the different image acquisition modalities and high-throughput technologies [1,2]. In these research fields, there is thus an increasing need for the application of cutting-edge computational approaches that generally involve Machine Learning or Computational Intelligence techniques, able to provide high-performance and specialized services in medical contexts [3]. Machine Learning and Computational Intelligence techniques can effectively perform image processing operations (such as segmentation [4,5,6,7,8,9,10], classification [11,12,13,14], and quantification [15,16,17,18]), in the fields of neuroimaging and oncological imaging. Although manual approaches often remain the golden standard in several tasks, Machine Learning can be exploited to automate and facilitate the work of researchers and clinicians. In addition, these fields often present new clustering and classification challenges, as well as combinatorial problems, which can be effectively addressed using novel strategies based on Machine Learning and Computational Intelligence techniques.
More recently, Deep Learning approaches [4,5,7,11,14,19] were shown to be very successful in computer vision and bioinformatics tasks owing to their ability to automatically extract hierarchical descriptive features from input images or gene expression data. They have also been used in the oncological, neuroimaging, and microscopy imaging domains for automatic disease diagnosis [12,13], tissue segmentation [16,20], and even synthetic image generation. However, the main issue remains the relative sample paucity of the typical datasets that leads to a poor generalization of the employed deep Artificial Neural Networks, considering the high number of required parameters. Consequently, parameter-efficient design paradigms specifically tailored to biomedical applications ought to be devised, also by exploiting Computational Intelligence based techniques (e.g., Evolutionary Computation, Swarm Intelligence, and neuroevolution).
In this context, advanced Machine Learning techniques were suitably exploited to combine heterogeneous sources of information, allowing for multiomics data integration [21,22]. Such kinds of analyses represent a significant step towards personalized medicine.

Author Contributions

Conceptualization, L.R., A.T. and C.M.; writing—original draft preparation, L.R., A.T. and C.M.; writing—review and editing, L.R., A.T. and C.M.; visualization, L.R. and A.T.; supervision, C.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rundo, L.; Militello, C.; Vitabile, S.; Russo, G.; Sala, E.; Gilardi, M.C. A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration. Fund. Inform. 2019, 171, 345–365. [Google Scholar] [CrossRef]
  2. Castiglioni, I.; Rundo, L.; Codari, M.; Di Leo, G.; Salvatore, C.; Interlenghi, M.; Gallivanone, F.; Cozzi, A.; D’Amico, N.C.; Sardanelli, F. AI Applications to Medical Images: From Machine Learning to Deep Learning. Phys. Med. 2021, 83, 9–24. [Google Scholar] [CrossRef] [PubMed]
  3. Conti, V.; Militello, C.; Rundo, L.; Vitabile, S. A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks. IEEE Trans. Emerg. Top. Comput. 2021, 9, 1709–1722. [Google Scholar] [CrossRef]
  4. Weis, C.-A.; Weihrauch, K.R.; Kriegsmann, K.; Kriegsmann, M. Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination? Appl. Sci. 2022, 12, 3718. [Google Scholar] [CrossRef]
  5. Park, S.; Kim, H.; Shim, E.; Hwang, B.-Y.; Kim, Y.; Lee, J.-W.; Seo, H. Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images. Appl. Sci. 2022, 12, 1358. [Google Scholar] [CrossRef]
  6. Militello, C.; Ranieri, A.; Rundo, L.; D’Angelo, I.; Marinozzi, F.; Bartolotta, T.V.; Bini, F.; Russo, G. On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI. Appl. Sci. 2021, 12, 162. [Google Scholar] [CrossRef]
  7. Wu, S.; Wu, Y.; Chang, H.; Su, F.T.; Liao, H.; Tseng, W.; Liao, C.; Lai, F.; Hsu, F.; Xiao, F. Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery. Appl. Sci. 2021, 11, 9180. [Google Scholar] [CrossRef]
  8. Militello, C.; Rundo, L.; Dimarco, M.; Orlando, A.; Conti, V.; Woitek, R.; D’Angelo, I.; Bartolotta, T.V.; Russo, G. Semi-Automated and Interactive Segmentation of Contrast-Enhancing Masses on Breast DCE-MRI Using Spatial Fuzzy Clustering. Biomed. Signal Process. Control. 2022, 71, 103113. [Google Scholar] [CrossRef]
  9. Militello, C.; Vitabile, S.; Rundo, L.; Russo, G.; Midiri, M.; Gilardi, M.C. A Fully Automatic 2D Segmentation Method for Uterine Fibroid in MRgFUS Treatment Evaluation. Comput. Biol. Med. 2015, 62, 277–292. [Google Scholar] [CrossRef]
  10. Isensee, F.; Jaeger, P.F.; Kohl, S.A.A.; Petersen, J.; Maier-Hein, K.H. nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation. Nat. Methods 2021, 18, 203–211. [Google Scholar] [CrossRef]
  11. Asami, Y.; Yoshimura, T.; Manabe, K.; Yamada, T.; Sugimori, H. Development of Detection and Volumetric Methods for the Triceps of the Lower Leg Using Magnetic Resonance Images with Deep Learning. Appl. Sci. 2021, 11, 12006. [Google Scholar] [CrossRef]
  12. Baazaoui, H.; Hubertus, S.; Maros, M.E.; Mohamed, S.A.; Förster, A.; Schad, L.R.; Wenz, H. Artificial Neural Network-Derived Cerebral Metabolic Rate of Oxygen for Differentiating Glioblastoma and Brain Metastasis in MRI: A Feasibility Study. Appl. Sci. 2021, 11, 9928. [Google Scholar] [CrossRef]
  13. Taibouni, K.; Miere, A.; Samake, A.; Souied, E.; Petit, E.; Chenoune, Y. Choroidal Neovascularization Screening on OCT-Angiography Choriocapillaris Images by Convolutional Neural Networks. Appl. Sci. 2021, 11, 9313. [Google Scholar] [CrossRef]
  14. Karhade, J.; Ghosh, S.K.; Gajbhiye, P.; Tripathy, R.K.; Rajendra Acharya, U. Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal. Appl. Sci. 2021, 11, 7920. [Google Scholar] [CrossRef]
  15. Zhang, J.; Huang, Y.; Ye, F.; Yang, B.; Li, Z.; Hu, X. Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning. Appl. Sci. 2022, 12, 4796. [Google Scholar] [CrossRef]
  16. Sharma, M.; Goudar, V.S.; Koduri, M.P.; Tseng, F.G.; Bhattacharya, M. Quantitative and Qualitative Image Analysis of In Vitro Co-Culture 3D Tumor Spheroid Model by Employing Image-Processing Techniques. Appl. Sci. 2021, 11, 4636. [Google Scholar] [CrossRef]
  17. Rundo, L.; Tangherloni, A.; Militello, C.; Gilardi, M.C.; Mauri, G. Multimodal Medical Image Registration Using Particle Swarm Optimization: A Review. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece, 6–9 December 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–8. [Google Scholar]
  18. Rundo, L.; Militello, C.; Vitabile, S.; Casarino, C.; Russo, G.; Midiri, M.; Gilardi, M.C. Combining Split-and-Merge and Multi-Seed Region Growing Algorithms for Uterine Fibroid Segmentation in MRgFUS Treatments. Med. Biol. Eng. Comput. 2016, 54, 1071–1084. [Google Scholar] [CrossRef]
  19. Lee, J.; Chung, S.W. Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future. Appl. Sci. 2022, 12, 681. [Google Scholar] [CrossRef]
  20. Fasoula, A.; Duchesne, L.; Cano, J.D.G.; Moloney, B.M.; Abd Elwahab, S.M.; Kerin, M.J. Automated Breast Lesion Detection and Characterization with the Wavelia Microwave Breast Imaging System: Methodological Proof-of-Concept on First-in-Human Patient Data. Appl. Sci. 2021, 11, 9998. [Google Scholar] [CrossRef]
  21. Simidjievski, N.; Bodnar, C.; Tariq, I.; Scherer, P.; Andres Terre, H.; Shams, Z.; Jamnik, M.; Liò, P. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Front. Genet. 2019, 10, 1205. [Google Scholar] [CrossRef] [Green Version]
  22. Tangherloni, A.; Ricciuti, F.; Besozzi, D.; Liò, P.; Cvejic, A. Analysis of Single-Cell RNA Sequencing Data Based on Autoencoders. BMC Bioinform. 2021, 22, 309. [Google Scholar] [CrossRef] [PubMed]
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