Hyperspectral Imaging in der Kopf-Hals-Onkologie^[*]

Zusammenfassung

Hyperspectral Imaging ist eine in anderen (natur)wissenschaftlichen Bereichen (z. B. Erdfernerkundung) schon lange etablierte Methode zur objektiven Klassifikation von Bildinforma­tion, die vor etwa 10 Jahren erstmals im Bereich der Humanmedizin angewandt wurde. Aufgrund der immanenten Vorteile – nicht-destruktive Probengewinnung, Anwendbarkeit mit den gängigen optischen Modalitäten (Mikroskop, Endoskop), Objektivierbarkeit, und Untersucher-Unabhängigkeit – wurden in der Zwischenzeit verschiedene Ansätze entwickelt, die das Potenzial des Hyperspectral Imaging für die Behandlung von Krebspatienten nutzen wollen. In dieser Übersichtsarbeit wird erläutert, welche Methoden für äquivalente Fragestellungen in anderen Bereichen bestehen, wie diese auf die Humanmedizin übertragen werden können und welche Perspektiven sich daraus insbesondere für den Bereich der traditionell innovativen Kopf-Hals-Onkologie ergeben.

Abstract

Hyperspectral Imaging in Head & Neck Oncology

Hyperspectral Imaging has long been established in other scientific disciplines than medicine (e. g. earth science) as a means for objective classification of image data information. Some 10 years ago it was first introduced into medicine. Due to its immanent advantages – non-destructive specimen, compatibility with established optical tools (microscope, endoscope), objectivity, and user-independence – several attempts have been made in order to use its potential for the treatment of cancer patients. This publication reviews which methods have been developed for analogue issues in disciplines other than medicine, how these can be transferred into medicine, and what the perspectives are for the traditional innovative field of head-and-neck-oncology.

Schlüsselwörter

Hyperspectral Imaging - Kopf-Hals-Onkologie - Endoskopie - Mikroskopie - Klassifikation

Key words

hyperspectral imaging - head-neck-oncology - endoscopy - microscopy - classification

^* Die Arbeitsgruppe wird durch die Deutsche Krebshilfe im Rahmen des Projektes 109825 „Früherkennung von Kehlkopfkrebs mittels hyperspektralem Imaging“ unterstützt.

• Literatur

• 1 Goetz AFH, Rowan LC. Geologic remote sensing. Science 1981; 211: 781-791
• 2 Goetz AFH, Vane G, Solomon JE, Rock BN. Imaging spectrometry for remote earth sensing. Science 1985; 228: 1147-1153
• 3 Rajendran S, Rajamanickam M. Hyperspectral remote sensing – a development and perspective. In: Rajendran S, Aravinda S, Jeyavel Rajakumar T, Sivakumar R, Murali Mohan KR. Hrsg Hyperspectral remote sensing and spectral signature applications. New India Publishing Agency; New Delhi: 2009: 1-9
• 4 Camps-Valls G, Bandos Marsheva TV, Zhou D. Semi-supervised graphbased hyperspectral image classification. Transactions on Geoscience and Remote Sensing 2007; 45: 3044-3054
• 5 Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, Faust JA, Pavri BE, Chovit CJ, Solis M, Olah MR. Imaging spectroscopy and the airborne visible infrared imaging spectrometer (AVIRIS). Remote Sens Environ 1998; 65: 227-248
• 6 Rickard LJ, Basedow RW, Zalewski EF, Silverglate PR, Landers M. HYDICE: an airborne system for hyperspectral imaging. In: Orlando VG. Hrsg Proceedings of SPIE. SPIE Press; San Diego: 1993. 1937. 173-179
• 7 Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton JC, Trianni G. Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 2009; 113: 110-122
• 8 Stein DWJ, Beaven SG, Hoff LE, Winter EM, Schaum AP, Stocker AD. Anomaly detection from hyperspectral imagery. Signal Processing Magazine 2002; 19: 58-69
• 9 Healey G, Slater D. Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions. Transactions on Geoscience and Remote Sensing 1999; 37: 2706-2717
• 10 Caefer CE, Rotman SR, Silverman J, Yip PW. Algorithms for point target detection in hyperspectral imagery. In: Shen S. Hrsg Proceedings of SPIE. SPIE Press; San Diego: 2002. 4816. 424-457
• 11 Gong P, Pu R, Yu B. Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sens Environ 1997; 62: 189-200
• 12 Hamada Y, Stow DA, Coulter LL, Jafolla JC, Hendricks LW. Detecting Tamarisk species (Tamarix spp.) in riparian habitats of Southern California using high spatial resolution hyperspectral imagery. Remote Sens Environ 2007; 109: 237-248
• 13 Kalacska M, Bohlman S, Sanchez-Azofeifa GA, Castro-Esau K, Caello T. Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sens Environ 2007; 109: 406-415
• 14 Zarco-Tejada PJ, González-Dugo V, Berni JAJ. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 2012; 117: 322-337
• 15 Glenn NF, Mundt JT, Weber KT, Prather TS, Lass LW, Pettingill J. Hyperspectral data processing for repeat detection of small infestations of leafy spurge. Remote Sens Environ 2005; 95: 399-412
• 16 Prabhakar M, Prasad YG, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Computers and Electronics in Agriculture 2011; 79: 189-198
• 17 Silvestri S, Marani M, Marani A. Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography. Physics and chemistry of the Earth 2003; 28: 15-25
• 18 Kong SG, Chen YR, Kim MS. Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. Applied Optics 2004; 43: 824-833
• 19 Bannon D, Van Veen C. Hyperspectral Imaging gets stamp of approval for food processing. Photonics Spectra 2012; 44-48 (Publikation ohne peer review, jedoch gute Darstellung praktischer Anwendungen im Bereich der Lebensmittelindustrie)
• 20 Mahlein A-K, Oerke E-C, Steiner U, Dehne H-W. Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 2012; 133: 197-209
• 21 Mahlein A-K, Steiner U, Dehne H-W, Oerke E-C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agric 2010; 11: 413-431
• 22 Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 2012; 8: 3
• 23 Mahlein A-K, Rumpf T, Welke P, Dehne H-W, Plümer L, Steiner U, Oerke E-C. Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 2013; 128: 21-30
• 24 Rumpf T, Mahlein A-K, Steiner U, Oerke E-C, Dehne H-W, Plümer L. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 2010; 74: 91-99
• 25 Schmidt K. Analyse hyperspektraler Signaturen mit doppelten Weibull-Funktionen. PFG 2011; 5: 0349-0359
• 26 Farkas DL, Becker D. Applications of spectral imaging: detection and analysis of human melanoma and its precursors. Pigment Cell Res 2001; 14: 2-8
• 27 Ferris DG, Lawhead RA, Dickmann ED, Holtzapple N, Miller JA, Grogan S, Bambot S, Agrawal A, Faupel ML. Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia. J Lower Genital Tract Disease 2001; 5: 65-72
• 28 Harris AT. Spectral mapping tools from the earth sciences applied to spectral microscopy data. Cytometry Part A 2006; 69A: 872-879
• 29 Schubert JM, Mazur AI, Bird B, Miljkovic M, Diem M. Single point vs. mapping approach for spectral cytopathology (SCP). J Biophoton 2010; 3: 588-596
• 30 Siddiqi AM, Li H, Faruque F, William W, Lai K, Hughson M, Bigler S, Beach J, Johnson W. Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells. Cancer (Cancer Cytopathol) 2008; 114: 13-21
• 31 Uhr JW, Huebschman ML, Frenkel EP, Lane NL, Ashfaq R, Liu H, Rana DR, Cheng L, Lin AT, Hughes GA, Zhang XJ, Garner HR. Molecular profiling of individual tumor cells by hyperspectral microscopic imaging. Translational Research 2012; 159: 336-375
• 32 Martin ME, Wabuyele MB, Chen K, Kasili P, Panjehpour M, Phan M, Overholt B, Cunningham G, Wilson D, DeNovo RC, Vo-Dinh T. Development of an advanced hyperspectral imaging (HSI) system with applications for cancer detection. Annals of Biomedical Engineering 2006; 34: 1061-1068
• 33 Kiyotoki S, Nishikawa J, Okamoto T, Hamabe K, Saito M, Goto A, Fujita Y, Hamamoto Y, Takeuchi Y, Satori S, Sakaida I. New method for detection of gastric cancer by hyperspectral imaging: a pilot study. J Biomed Optics 2013; 18: 026010
• 34 Panasyuk S, Yang S, Faller DV, Ngo D, Lew RA, Freeman JE, Rogers AE. Medical hyperspectral imaging to facilitate residual tumor identification during surgery. Caner Biology Therapy 2007; 6: 439-446
• 35 Kester RT, Bedard N, Gao L, Tkaczyk TS. Real-time snapshot hyperspectral imaging endoscope. Journal of Biomedical Optics 2011; 16: 056005
• 36 Muldoon TJ, Roblyer D, Williams MD, Stepanek VMT, Richards-Kortum R, Gillenwater AM. Noninvasive imaging of oral neoplasia with a high-resolution fiber-optic microendoscope. Head Neck 2012; 34: 305-312
• 37 Liu Z, Wang H, Li Q. Tongue tumor detection in medical hyperspectral images. Sensors 2012; 12: 162-174
• 38 Gerstner AOH, Laffers W, Bootz F, Farkas DL, Martin R, Bendix J, Thies B. Hyperspectral imaging of mucosal surfaces in patients. J Biophotonics 2012; 5: 255-262
• 39 Gerstner AOH, Laffers W, Schade G, Göke F, Martin R, Thies B. Endoskopie des Larynx mit Hyperspectral Imaging. HNO 2012; 60: 1047-1052
• 40 Martin R, Thies B, Gerstner AOH. Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx. Int J Health Geogr 2012; 11: 21
• 41 Bücheler BM, Ehnes A, Kavsadze M, Langenberg S, Wilhelm-Buchstab T, Zipfel M, Keiner S, Gerstner AOH. Lebensqualität nach Therapie von Kopf-Hals-Tumoren – Longitudinaler Vergleich von Operation und adjuvanter Radio(chemo)therapie. HNO 2012; 60: 1053-1059
• 42 Arens C, Vorwerk U, Just T, Betz CS, Kraft M. Fortschritte der endoskopischen Diagnostik von Dysplasien und Karzinomen des Larynx. HNO 2012; 60: 44-52
• 43 Upile T, Jerjes WK, Sterenborg HJ, Wong BJ, El-Naggar AK, Ilgner JF, Sandison A, Witjes MJ, Biel MA, van Veen R, Hamdoon Z, Gillenwater A, Mosse CA, Robinson DJ, Betz CS, Stepp H, Bolotine L, McKenzie G, Barr H, Chen Z, Berg K, D’Cruz AK, Sudhoff H, Stone N, Kendall C, Fisher S, MacRobert AJ, Leunig A, Olivo M, Richards-Kortum R, Soo KC, Bagnato V, Choo-Smith L-P, Svanberg K, Tan IB, Wilson BC, Wolfsen H, Bigio I, Yodh AG, Hopper C. At the frontiers of surgery. Head & Neck Oncology 2011; 3: 7
• 44 Arens C, Dreyer T, Glanz H, Malzahn K. Indirect autofluorescence laryngoscopy in the diagnosis of laryngeal cancer and its precursor lesions. Eur Arch Otorhinolaryngol 2004; 261: 71-76
• 45 Kraft M, Glanz H, von Gerlach S, Wisweh H, Lubatschowski H, Arens C. Morphologische Klassifikation des Reinke-Ödems mittels optischer Kohärenztomografie. Laryngo-Rhino-Otol 2010; 89: 224-227
• 46 Just T, Stave J, Kreutzer HJ, Guthoff R, Pau HW. Konfokalmiroskopische Untersuchungen des Kehlkopfepithels. Laryngorhinootologie 2007; 86: 644-648
• 47 Bajbouj M. Diagnostik und Therapie atypischer Refluxsymptome bei fehlendem PPI-Ansprechen. HNO 2012; 60: 193-199