Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Wydawca
Czasopismo
Rocznik
Tom
Strony
345--365
Opis fizyczny
Bibliogr. 134 poz., rys., wykr.
Twórcy
autor
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, Cambridge, UK
autor
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy
autor
- Department of Biomedicine Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
autor
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy
autor
- Department of Radiology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, Cambridge, UK
autor
- Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalù (PA), Italy
- Cancer Research UK Cambridge Centre, Cambridge, UK.
Bibliografia
- [1] Mitra S, Shankar BU. Medical image analysis for cancer management in natural computing framework. Inform. Sci., 2015. 306:111-131. doi:10.1016/j.ins.2015.02.015.
- [2] Kar AK. Bio inspired computing-a review of algorithms and scope of applications. Expert Syst. Appl., 2016. 59:20-32. doi:10.1016/j.eswa.2016.04.018.
- [3] Hidalgo-Herrero M, Rabanal P, Rodriguez I, Rubio F. Comparing problem solving strategies for NP-hard optimization problems. Fundam. Inform., 2013. 124(1-2):1-25. doi:10.3233/FI-2013-822.
- [4] Siddique N, Adeli H. Nature inspired computing: an overview and some future directions. Cogn. Computat., 2015. 7(6):706-714. doi:10.1007/s12559-015-9370-8.
- [5] Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics, 2008. 24(17):1827-1836. doi:10.1093/bioinformatics/btn346.
- [6] Shamir L, Delaney JD, Orlov N, Eckley DM, Goldberg IG. Pattern recognition software and techniques for biological image analysis. PLoS Comput. Biol., 2010. 6(11):e1000974. doi:10.1371/journal.pcbi.1000974.
- [7] Gray JE, Feldman H, Reti S, Markson L, Lu X, Davis RB, Safran CA. Using Digital Crumbs from an Electronic Health Record to identify, study and improve health care teams. In: AMIA Annu. Symp. Proc., volume 2011. American Medical Informatics Association, 2011 p. 491.
- [8] Alepis E, Lambrinidis C. M-health: supporting automated diagnosis and electronic health records. SpringerPlus, 2013. 2(1):103. doi:10.1186/2193-1801-2-103.
- [9] Brady M, Highnam R, Irving B, Schnabel JA. Oncological image analysis. Med. Image Anal., 2016. 33:7-12. doi:10.1016/j.media.2016.06.012.
- [10] Serbanati LD, Ricci FL, Mercurio G, Vasilateanu A. Steps towards a digital health ecosystem. J. Biomed. Inform., 2011. 44(4):621-636. doi:10.1016/j.jbi.2011.02.011.
- [11] Meijering E, Carpenter AE, Peng H, Hamprecht FA, Olivo-Marin JC. Imagining the future of bioimage analysis. Nat. Biotechnol., 2016. 34(12):1250. doi:10.1038/nbt.3722.
- [12] Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng., 2017. 19:221-248. doi:10.1146/annurev-bioeng-071516-044442.
- [13] Kari L, Rozenberg G. The many facets of natural computing. Commun. ACM, 2008. 51(10):72-83. doi:10.1145/1400181.1400200.
- [14] Yankeelov TE, Mankoff DA, Schwartz LH, Lieberman FS, Buatti JM, Mountz JM, Erickson BJ, Fennessy FM, Huang W, Kalpathy-Cramer J, et al. Quantitative imaging in cancer clinical trials. Clin. Cancer Res., 2016. 22(2):284-290. doi:10.1158/1078-0432.CCR-14-3336.
- [15] Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun., 2014. 5:4006.
- [16] Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC. Machine learning in medical imaging. IEEE Signal Process. Mag., 2010. 27(4):25-38. doi:10.1109/MSP.2010.936730.
- [17] Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat. Rev. Cancer, 2018. 18(8):500-510. doi:10.1038/s41568-018-0016-5.
- [18] Lahat D, Adali T, Jutten C. Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE, 2015. 103(9):1449-1477. doi:10.1109/JPROC.2015.2460697.
- [19] Serra A, Galdi P, Tagliaferri R. Machine learning for bioinformatics and neuroimaging. Wiley Interdisc. Rev. Data Min. Knowl. Discov., 2018. p. e1248. doi:10.1002/widm.1248.
- [20] Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, Sala E, Hricak H, Deasy JO. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc. Natl. Acad. Sci., 2015. 112(46):E6265-E6273. doi:10.1073/pnas.1505935112.
- [21] Alaa AM, Moon KH, Hsu W, van der Schaar M. ConfidentCare: a clinical decision support system for personalized breast cancer screening. IEEE Trans. Multimed., 2016. 18(10):1942-1955. doi:10.1109/TMM.2016.2589160.
- [22] Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat. Rev. Genet., 2015. 16(6):321. doi:10.1038/nrg3920.
- [23] Capobianco E, Liò P. Comorbidity networks: beyond disease correlations. J. Complex Netw., 2015. 3(3):319-332. doi:10.1093/comnet/cnu048.
- [24] James AP, Dasarathy BV. Medical image fusion: a survey of the state of the art. Inform. Fusion, 2014. 19:4-19. doi:10.1016/j.inffus.2013.12.002.
- [25] Soille PJ, Ansoult MM. Automated basin delineation from digital elevation models using mathematical morphology. Signal Process., 1990. 20(2):171-182. doi:10.1016/0165-1684(90)90127-K.
- [26] Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell., 1991. 13(6):583-598. doi:10.1109/34.87344.
- [27] Beucher S, Meyer F. The morphological approach to segmentation: the watershed transformation. In: Mathematical Morphology in Image Processing, volume 34, pp. 433-481. Marcel Dekker Inc., New York. NY, USA, 1993.
- [28] Grau V, Mewes A, Alcaniz M, Kikinis R, Warfield SK. Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Imaging, 2004. 23(4):447-458. doi:10.1109/TMI.2004.824224.
- [29] Grady L. Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 2006. 28(11):1768-1783. doi:10.1109/TPAMI.2006.233.
- [30] Stefano A, Vitabile S, Russo G, Ippolito M, Marletta F, D’Arrigo C, D’Urso D, Gambino O, Pirrone R, Ardizzone E, et al. A fully automatic method for biological target volume segmentation of brain metastases. Int. J. Imaging Syst. Technol., 2016. 26(1):29-37. doi:10.1002/ima.22154.
- [31] Stefano A, Vitabile S, Russo G, Ippolito M, Sabini MG, Sardina D, Gambino O, Pirrone R, Ardizzone E, Gilardi MC. An enhanced random walk algorithm for delineation of head and neck cancers in PET studies. Med. Biol. Eng. Comput., 2017. 55(6):897-908. doi:10.1007/s11517-016-1571-0.
- [32] Rundo L, Stefano A, Militello C, Russo G, Sabini MG, D’Arrigo C, Marletta F, Ippolito M, Mauri G, Vitabile S, Gilardi MC. A fully automatic approach for multimodal PET and MR image segmentation in Gamma Knife treatment planning. Comput. Methods Programs Biomed., 2017. 144:77-96. doi:10.1016/j.cmpb.2017.03.011.
- [33] Militello C, Rundo L, Vitabile S, Russo G, Pisciotta P, Marletta F, Ippolito M, D’Arrigo C, Midiri M, Gilardi MC. Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised fuzzy c-means clustering. Int. J. Imaging Syst. Technol., 2015. 25(3):213-225. doi:10.1002/ima.22139.
- [34] Rundo L, Militello C, Vitabile S, Russo G, Pisciotta P, Marletta F, Ippolito M, DArrigo C, Midiri M, Gilardi MC. Semi-automatic brain lesion segmentation in Gamma Knife treatments using an unsupervised fuzzy c-means clustering technique. In: Advances in Neural Networks: Computational Intelligence for ICT, volume 54 of Smart Innovation, Systems and Technologies, pp. 15-26. Springer, 2016. doi:10.1007/978-3-319-33747-0_2.
- [35] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science, 1983. 220(4598):671-680. doi:10.1126/science.220.4598.671.
- [36] Glover F. Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res., 1986. 13(5):533-549. doi:10.1016/0305-0548(86)90048-1.
- [37] Dou TH, Min Y, Neylon J, Thomas D, Kupelian P, Santhanam AP. Fast simulated annealing and adaptive Monte Carlo sampling based parameter optimization for dense optical-flow deformable image registration of 4DCT lung anatomy. In: Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, volume 9786 of Prof. SPIE. International Society for Optics and Photonics, 2016 p. 97860N. doi:10.1117/12.2217194.
- [38] Wachowiak MP, Elmaghraby AS. The continuous tabu search as an optimizer for 2D-to-3D biomedical image registration. In: Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 2208 of LNCS. Springer, 2001 pp. 1273-1274. doi:10.1007/3-540-45468-3_191.
- [39] Rozenberg G, Bck T, Kok JN. Handbook of Natural Computing. Springer Publishing Company, Incorporated, Berlin-Heidelberg, Germany, 1 edition, 2011. ISBN 978-3-540-92909-3.
- [40] Von Neumann J, Burks AW. Theory of self-reproducing automata. University of Illinois Press, Urbana, IL, USA, 1966.
- [41] Kari J. Theory of cellular automata: a survey. Theor. Comput. Sci., 2005. 334(1-3):3-33. doi:10.1016/j.tcs.2004.11.021.
- [42] Hamamci A, Kucuk N, Karaman K, Engin K, Unal G. Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging, 2012. 31(3):790-804. doi:10.1109/TMI.2011.2181857.
- [43] Sinop AK, Grady L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: 11th International Conference on Computer Vision (ICCV) 2007. IEEE, 2007 pp. 1-8. doi:10.1109/ICCV.2007.4408927.
- [44] Rundo L, Militello C, Russo G, Pisciotta P, Valastro LM, Sabini MG, Vitabile S, Gilardi MC, Mauri G. Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model. In: Proc. 12th International Conference on Cellular Automata for Research and Industry (ACRI), volume 9863 of LNCS. Springer, 2016 pp. 323-333. doi:10.1007/978-3-319-44365-2_32.
- [45] Rundo L, Militello C, Russo G, Vitabile S, Gilardi MC, Mauri G. GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model. Nat. Comput., 2018. 17(3):521-536. doi:10.1007/s11047-017-9636-z.
- [46] Rother C, Kolmogorov V, Blake A. GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph., 2004. 23(3):309-314. doi:10.1145/1186562.1015720.
- [47] Păun G. Computing with membranes. J. Comput. Syst. Sci., 2000. 61(1):108-143. doi:10.1006/jcss.1999.1693.
- [48] Nobile MS, Porreca AE, Spolaor S, Manzoni L, Cazzaniga P, Mauri G, Besozzi D. Efficient simulation of reaction systems on Graphics Processing Units. Fundam. Inform., 2017. 154(1-4):307-321. doi:10.3233/FI-2017-1568.
- [49] Yahya RI, Shamsuddin SM, Yahya SI, Hasan S, Al-Salibi B, Al-Khafaji G. Image segmentation using membrane computing: a literature survey. In: Gong M, Pan L, Song T, Zhang G (eds.), Bio-Inspired Computing - Theories and Applications (BIC-TA), volume 681 of Communications in Computer and Information Science. Springer, 2016 pp. 314-335. doi:10.1007/978-981-10-3611-8_26.
- [50] Díaz-Pernil D, Berciano A, Peña-Cantillana F, Gutíérrez-Naranjo MA. Segmenting images with gradient-based edge detection using membrane computing. Pattern Recogn. Lett., 2013. 34(8):846-855. doi:10.1016/j.patrec.2012.10.014.
- [51] Peng H, Wang J, Pérez-Jiménez MJ, Shi P. A novel image thresholding method based on membrane computing and fuzzy entropy. J. Intell. Fuzzy Syst., 2013. 24(2):229-237. doi:10.3233/IFS-2012-0549.
- [52] Eiben AE, Smith J. From evolutionary computation to the evolution of things. Nature, 2015. 521(7553):476-482. doi:10.1038/nature14544.
- [53] Rundo L, Militello C, Russo G, Garufi A, Vitabile S, Gilardi MC, Mauri G. Automated prostate gland segmentation based on an unsupervised fuzzy c-means clustering technique using multispectral T1w and T2w MR imaging. Information, 2017. 8(2):49. doi:10.3390/info8020049.
- [54] Luong NH, Alderliesten T, Bel A, Niatsetski Y, Bosman PA. Application and benchmarking of multiobjective evolutionary algorithms on high-dose-rate brachytherapy planning for prostate cancer treatment. Swarm Evol. Comput., 2018. 40:37-52. doi:10.1016/j.swevo.2017.12.003.
- [55] Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 1992. ISBN 0262082136.
- [56] Rundo L, Tangherloni A, Nobile MS, Militello C, Besozzi D, Mauri G, Cazzaniga P. MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl., 2019. 119:387-399. doi:10.1016/j.eswa.2018.11.013.
- [57] Hashemi S, Kiani S, Noroozi N, Moghaddam ME. An image contrast enhancement method based on genetic algorithm. Pattern Recognit. Lett., 2010. 31(13):1816-1824. doi:10.1016/j.patrec.2009.12.006.
- [58] Rundo L, Tangherloni A, Cazzaniga P, Nobile MS, Russo G, Gilardi MC, Vitabile S, Mauri G, Besozzi D, Militello C. A novel framework for MR image segmentation and quantification by using MedGA. Comput. Methods Programs Biomed., 2019. 176:159-172. doi:10.1016/j.cmpb.2019.04.016.
- [59] Maulik U. Medical image segmentation using genetic algorithms. IEEE Trans. Inform. Technol. Biomed., 2009. 13(2):166-173. doi:10.1109/TITB.2008.2007301.
- [60] Cagnoni S, Dobrzeniecki AB, Poli R, Yanch JC. Genetic algorithm-based interactive segmentation of 3D medical images. Image Vis. Comput., 1999. 17(12):881-895. doi:10.1016/S0262-8856(98)00166-8.
- [61] Antonelli M, Cardoso MJ, Johnston EW, Appayya MB, Presles B, Modat M, Punwani S, Ourselin S. GAS: a genetic atlas selection strategy in multi-atlas segmentation framework. Med. Image Anal., 2019. 52:97-108. doi:10.1016/j.media.2018.11.007.
- [62] Ortiz A, Górriz J, Ramírez J, Salas-Gonzalez D, Llamas-Elvira JM. Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Appl. Soft Comput., 2013. 13(5):2668-2682. doi:10.1016/j.asoc.2012.11.020.
- [63] Aličković E, Subasi A. Breast cancer diagnosis using GA feature selection and rotation forest. Neural Comput. Applic., 2017. 28(4):753-763. doi:10.1007/s00521-015-2103-9.
- [64] Koza JR. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1 edition, 1992. ISBN 0262111705.
- [65] Bianco S, Ciocca G, Schettini R. Combination of video change detection algorithms by genetic programming. IEEE Trans. Evol. Comput., 2017. 21(6):914-928. doi:10.1109/TEVC.2017.2694160.
- [66] Vanneschi L, Castelli M, Silva S. A survey of semantic methods in genetic programming. Genet. Program. Evolvable Mach., 2014. 15(2):195-214. doi:10.1007/s10710-013-9210-0.
- [67] Castelli M, Trujillo L, Vanneschi L, Popovič A. Prediction of relative position of CT slices using a computational intelligence system. Appl. Soft Comput., 2016. 46:537-542. doi:10.1016/j.asoc.2015.09.021.
- [68] Poli R. Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl., 2008. 2008:2-10. doi:10.1155/2008/685175.
- [69] Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 1997. 11(4):341-359. doi:10.1023/A:1008202821328.
- [70] Lei B, Tan EL, Chen S, Ni D, Wang T, Lei H. Reversible watermarking scheme for medical image based on differential evolution. Expert Syst. Appl., 2014. 41(7):3178-3188. doi:10.1016/j.eswa.2013.11.019.
- [71] Eberhart RC, Shi Y, Kennedy J. Swarm Intelligence. Morgan Kaufmann, Burlington, MA, USA, 1 edition, 2001. ISBN 1558605959.
- [72] Kennedy J, Eberhart R. Particle swarm optimization. In: Proc. International Conference on Neural Networks (ICNN), volume 4. IEEE, 1995 pp. 1942-1948. doi:10.1109/ICNN.1995.488968.
- [73] Rundo L, Tangherloni A, Militello C, Gilardi MC, Mauri G. Multimodal medical image registration using particle swarm optimization: a review. In: Proc. Symposium Series on Computational Intelligence (SSCI). IEEE, 2016 pp. 1-8. doi:10.1109/SSCI.2016.7850261.
- [74] Wachowiak MP, Smolíková R, Zheng Y, Zurada JM, Elmaghraby AS, et al. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans. Evol. Comput., 2004. 8(3):289-301. doi:10.1109/TEVC.2004.826068.
- [75] Abdel-Basset M, Fakhry AE, El-Henawy I, Qiu T, Sangaiah AK. Feature and intensity based medical image registration using particle swarm optimization. J. Med. Syst., 2017. 41(12):197. doi:10.1007/s10916-017-0846-9.
- [76] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim., 2007. 39(3):459-471. doi:10.1007/s10898-007-9149-x.
- [77] Zhang Y, Wu L. Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach. Entropy, 2011. 13(4):841-859. doi:10.3390/e13040841.
- [78] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern., Part B, 1996. 26(1):29-41. doi:10.1109/3477.484436.
- [79] Dorigo M, Stützle T. Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin JY (eds.), Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science, pp. 311-351. Springer, 2019. doi:10.1007/978-3-319-91086-4_10.
- [80] Pereira C, Gonçalves L, Ferreira M. Optic disc detection in color fundus images using ant colony optimization. Med. Biol. Eng. Comput., 2013. 51(3):295-303. doi:10.1007/s11517-012-0994-5.
- [81] Hassanien AE, Moftah HM, Azar AT, Shoman M. MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl. Soft Comput., 2014. 14:62-71. doi:10.1016/j.asoc.2013.08.011.
- [82] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys., 1943. 5(4):115-133. doi:10.1007/BF02478259.
- [83] Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybern., 1982. 43(1):59-69. doi:10.1007/BF00337288.
- [84] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015. 521(7553):436. doi:10.1038/nature145.
- [85] Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 1980. 36(4):193-202. doi:10.1007/BF00344251.
- [86] Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol., 1962. 160(1):106-154.
- [87] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med. Image Anal., 2017. 42:60-88. doi:10.1016/j.media.2017.07.005.
- [88] Rajchl M, Lee MC, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, et al. DeepCut: object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans. Med. Imaging, 2017. 36(2):674-683. doi:10.1109/TMI.2016.2621185.
- [89] Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med. Image Anal., 2017. 35:18-31. doi:10.1016/j.media.2016.05.004.
- [90] Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal., 2017. 36:61-78. doi:10.1016/j.media.2016.10.004.
- [91] Ronneberger O, Fischer P, Brox T. U-Net: convolutional Networks for biomedical image segmentation. In: Proc. Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS. Springer, 2015 pp. 234-241. doi:10.1007/978-3-319-24574-4_28.
- [92] Milletari F, Navab N, Ahmadi SA. V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proc. International Conference on 3D Vision (3DV). IEEE, 2016 pp. 565-571. doi:10.1109/3DV.2016.79.
- [93] Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D. Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal., 2019. 53:197-207. doi:10.1016/j.media.2019.01.012.
- [94] Pelt DM, Sethian JA. A mixed-scale dense convolutional neural network for image analysis. Proc. Natl. Acad. Sci., 2018. 115(2):254-259. doi:10.1073/pnas.1715832114.
- [95] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proc. Advances in Neural Information Processing Systems (NIPS). 2014 pp. 2672-2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.
- [96] Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proc. International Conference on Computer Vision (ICCV). IEEE, 2017 pp. 2242-2251. doi:10.1109/ICCV.2017.244.
- [97] Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC. Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems. 2017 pp. 5769-5779.
- [98] Ravì D, Wong C, Deligianni F, et al. Deep learning for health informatics. IEEE J. Biomed. Health Inform., 2017. 21(1):4-21. doi:10.1109/JBHI.2016.2636665.
- [99] Costa P, Galdran A, Meyer MI, Niemeijer M, Abràmoff M, Mendona AM, Campilho A. End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imaging, 2018. 37(3):781-791. doi:10.1109/TMI.2017.2759102.
- [100] Chuquicusma MJM, Hussein S, Burt J, Bagci U. How to fool radiologists with generative adversarial networks? A visual Turing test for lung cancer diagnosis. arXiv preprint arXiv:1710.09762v1, 2017.
- [101] Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018. 321:321-331. doi:10.1016/j.neucom.2018.09.013.
- [102] Han C, Hayashi H, Rundo L, Araki R, Shimoda W, Muramatsu S, et al. GAN-based synthetic brain MR image generation. In: Proc. International Symposium on Biomedical Imaging (ISBI). IEEE, 2018 pp. 734-738. doi:10.1109/ISBI.2018.8363678.
- [103] Prastawa M, Bullitt E, Gerig G. Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal., 2009. 13(2):297-311. doi:10.1016/j.media.2008.11.002.
- [104] Yao X. Evolving artificial neural networks. Proc. IEEE, 1999. 87(9):1423-1447. doi:10.1109/5.784219.
- [105] Stanley KO, D’Ambrosio DB, Gauci J. A hypercube-based encoding for evolving large-scale neural networks. Artificial Life, 2009. 15(2):185-212. doi:10.1162/artl.2009.15.2.15202.
- [106] Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol. Comput., 2002. 10(2):99-127. doi:10.1162/106365602320169811.
- [107] Calimeri F, Marzullo A, Stamile C, Terracina G. Blood vessel segmentation in retinal fundus images using Hypercube NeuroEvolution of augmenting topologies (HyperNEAT). In: Quantifying and Processing Biomedical and Behavioral Signals, volume 103 of Smart Innovation, Systems and Technologies, pp. 173-183. Springer, 2019. doi:10.1007/978-3-319-95095-2_17.
- [108] Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology. Radiology, 2013. 269(1):8-14. doi:10.1148/radiol.13122697.
- [109] Evanko D. Two pictures are better than one. Nat. Methods, 2008. 5(5):377. doi:10.1038/nmeth0508-377.
- [110] Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2015. 278(2):563-577. doi:10.1148/radiol.2015151169.
- [111] Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer, 2012. 48(4):441-446. doi:10.1016/j.ejca.2011.11.036.
- [112] Lambin P, Leijenaar RT, Deist TM, Peerlings J, de Jong EE, van Timmeren J, Sanduleanu S, Larue RT, Even AJ, Jochems A, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol., 2017. 14(12):749. doi:10.1038/nrclinonc.2017.141.
- [113] Rosenstein BS, West CM, Bentzen SM, Alsner J, Andreassen CN, Azria D, Barnett GC, Baumann M, Burnet N, Chang-Claude J, et al. Radiogenomics: radiobiology enters the era of big data and team science. Int. J. Radiat. Oncol. Biol. Phys., 2014. 89(4):709-713. doi:10.1016/j.ijrobp.2014.03.009.
- [114] Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton J, Snyder A, Weigelt B, Vargas HA. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin. Radiol., 2017. 72(1):3-10. doi:10.1016/j.crad.2016.09.013.
- [115] Brindle K. New approaches for imaging tumour responses to treatment. Nat. Rev. Cancer, 2008. 8(2):94. doi:10.1038/nrc2289.
- [116] Yuan Y, Failmezger H, Rueda OM, Ali HR, Gräf S, Chin SF, Schwarz RF, Curtis C, Dunning MJ, Bardwell H, et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med., 2012. 4(157):157ra143-157ra143. doi:10.1126/scitranslmed.3004330.
- [117] Tangherloni A, Spolaor S, Rundo L, Nobile MS, Cazzaniga P, Mauri G, Liò P, Merelli I, Besozzi D. GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinform., 2019. 20(Suppl 4):172. doi:10.1186/s12859-019-2691-y.
- [118] Hassanien AE, Abraham A, Peters JF, Schaefer G, Henry C. Rough sets and near sets in medical imaging: a review. IEEE Trans. Inf. Technol. Biomed., 2009. 13(6):955-968. doi:10.1109/TITB.2009.2017017.
- [119] Zadeh LA. Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst., 1996. 4(2):103-111. doi:10.1109/91.493904.
- [120] Militello C, Vitabile S, Rundo L, Russo G, Midiri M, Gilardi MC. A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation. Comput. Biol. Med., 2015. 62:277-292. doi:10.1016/j.compbiomed.2015.04.030.
- [121] Rundo L, Militello C, Tangherloni A, Russo G, Vitabile S, Gilardi MC, Mauri G. NeXt for neuroradiosurgery: a fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique. Int. J. Imaging Syst. Technol., 2018. 28(1):21-37. doi:10.1002/ima.22253.
- [122] Nobile MS, Cazzaniga P, Besozzi D, Colombo R, Mauri G, Pasi G. Fuzzy Self-Tuning PSO: a settings-free algorithm for global optimization. Swarm Evol. Comput., 2018. 39:70-85. doi:10.1016/j.swevo.2017.09.001.
- [123] Tangherloni A, Rundo L, Nobile MS. Proactive particles in swarm optimization: a settings-free algorithm for real-parameter single objective optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017 pp. 1940-1947. doi:10.1109/CEC.2017.7969538.
- [124] Nobile MS, Tangherloni A, Rundo L, Spolaor S, Besozzi D, Mauri G, Cazzaniga P. Computational intelligence for parameter estimation of biochemical systems. In: Proc. Congress on Evolutionary Computation (CEC). IEEE, 2018 pp. 1-8. doi:10.1109/CEC.2018.8477873.
- [125] Mastriani M. Quantum image processing? Quantum Inf. Process., 2017. 16(1):27. doi:10.1007/s11128-016-1457-y.
- [126] Yan F, Iliyasu AM, Jiang Z. Quantum computation-based image representation, processing operations and their applications. Entropy, 2014. 16(10):5290-5338. doi:10.3390/e16105290.
- [127] Tangherloni A, Rundo L, Spolaor S, Nobile M, Merelli I, Besozzi D, Mauri G, Cazzaniga P, Liò P. High performance computing for haplotyping: models and platforms. In: Mencagli G, et al. (eds.), Proc. 24th International European Conference on Parallel and Distributed Computing (Euro-Par 2018), Workshop on Advances in High-Performance Bioinformatics, Systems Biology (Med-HPC 2018), volume 11339 of LNCS. Springer, 2019 pp. 650-661. doi:10.1007/978-3-030-10549-5\_51.
- [128] Nobile MS, Cazzaniga P, Tangherloni A, Besozzi D. Graphics processing units in bioinformatics, computational biology and systems biology. Brief. Bioinform., 2016. 18(5):870-885. doi:10.1093/bib/bbw058.
- [129] Eklund A, Dufort P, Forsberg D, LaConte SM. Medical image processing on the GPU-past, present and future. Med. Image Anal., 2013. 17(8):1073-1094. doi:10.1016/j.media.2013.05.008.
- [130] Smistad E, Falch TL, Bozorgi M, Elster AC, Lindseth F. Medical image segmentation on GPUs-a comprehensive review. Med. Image Anal., 2015. 20(1):1-18. doi:10.1016/j.media.2014.10.012.
- [131] Tajbakhsh N, Shin JY, Gurudu SR, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging, 2016. 35(5):1299-1312. doi:10.1109/TMI.2016.2535302.
- [132] Criminisi A. Machine learning for medical images analysis. Med. Image Anal., 2016. 33:91-93. doi:10.1016/j.media.2016.06.002.
- [133] Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. J. Am. Med. Assoc., 2017. 318(6):517-518. doi:10.1001/jama.2017.7797.
- [134] Gambino O, Rundo L, Cannella V, Vitabile S, Pirrone R. A framework for data-driven adaptive GUI generation based on DICOM. J. Biomed. Inform., 2018. 88:37-52. doi:10.1016/j.jbi.2018.10.009.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d7e42031-d4e6-4ce9-8898-b71d555edc61