Three-dimensional reconstruction of light microscopy image sections: present and future

Abstract

Three-dimensional (3D) image reconstruction technologies can reveal previously hidden microstructures in human tissue. However, the lack of ideal, non-destructive cross-sectional imaging techniques is still a problem. Despite some drawbacks, histological sectioning remains one of the most powerful methods for accurate high-resolution representation of tissue structures. Computer technologies can produce 3D representations of interesting human tissue and organs that have been serial-sectioned, dyed or stained, imaged, and segmented for 3D visualization. 3D reconstruction also has great potential in the fields of tissue engineering and 3D printing. This article outlines the most common methods for 3D tissue section reconstruction. We describe the most important academic concepts in this field, and provide critical explanations and comparisons. We also note key steps in the reconstruction procedures, and highlight recent progress in the development of new reconstruction methods.

Keywords

microtomy / 3D imaging / computer-assisted image processing / 3D printing / tissue scaffold

References

[1] Dickinson ME. Multimodal imaging of mouse development: tools for the postgenomic era. Dev Dyn2006; 235(9): 2386-2400

[2] Handschuh S, Schwaha T, Metscher BD. Showing their true colors: a practical approach to volume rendering from serial sections. BMC Dev Biol2010; 10(1): 41

[3] Liu B, Gao XL, Yin HX, Luo SQ, Lu J. A detailed 3D model of the guinea pig cochlea. Brain Struct Funct2007; 212(2): 223-230

[4] Rau TS, Hussong A, Herzog A, Majdani O, Lenarz T, Leinung M. Accuracy of computer-aided geometric 3D reconstruction based on histological serial microgrinding preparation. Comput Methods Biomech Biomed Engin2011; 14(7): 581-594

[5] Liu R, Yin X, Li H, Shao Q, York P, He Y, Xiao T, Zhang J. Visualization and quantitative profiling of mixing and segregation of granules using synchrotron radiation X-ray microtomography and three dimensional reconstruction. Int J Pharm2013; 445(1-2): 125-133

[6] Metscher BD. MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiol2009; 9(1): 11

[7] Burton RA, Schneider JE, Bishop MJ, Hales PW, Bollensdorff C, Robson MD, Wong KC, Morris J, Quinn TA, Kohl P. Microscopic magnetic resonance imaging reveals high prevalence of third coronary artery in human and rabbit heart. Europace2012; 14(Suppl 5): v73-v81

[8] Hofman R, Segenhout JM, Wit HP. Three-dimensional reconstruction of the guinea pig inner ear, comparison of OPFOS and light microscopy, applications of 3D reconstruction. J Microsc2009; 233(2): 251-257

[9] Voie AH, Burns DH, Spelman FA. Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J Microsc1993; 170(3): 229-236

[10] Sharpe J. Optical projection tomography. Annu Rev Biomed Eng 2004; 6(1): 209-228

[11] Eriksson AU, Svensson C, Hörnblad A, Cheddad A, Kostromina E, Eriksson M, Norlin N, Pileggi A, Sharpe J, Georgsson F, Alanentalo T, Ahlgren U. Near infrared optical projection tomography for assessments of β-cell mass distribution in diabetes research. J Vis Exp2013; (71): e50238

[12] Vinegoni C, Fumene Feruglio P, Razansky D, Gorbatov R, Ntziachristos V, Sbarbati A, Nahrendorf M, Weissleder R. Mapping molecular agents distributions in whole mice hearts using born-normalized optical projection tomography. PLoS ONE2012; 7(4): e34427

[13] Mujawar LH, Maan AA, Khan MK, Norde W, van Amerongen A. Distribution of biomolecules in porous nitrocellulose membrane pads using confocal laser scanning microscopy and high-speed cameras. Anal Chem2013; 85(7): 3723-3729

[14] Hu W, Lux R, Shi W. Analysis of exopolysaccharides in Myxococcus xanthus using confocal laser scanning microscopy. Methods Mol Biol2013; 966: 121-131

[15] Nomoto T, Matsumoto Y, Toh K, Christie RJ, Miyata K, Oba M, Cabral H, Murakami M, Fukushima S, Nishiyama N, Kataoka K. Evaluation of the dynamics of drug delivery systems (DDS) using intravital real-time confocal laser scanning microscopy. Yakugaku Zasshi2012; 132(12): 1347-1354 (in Japanese)

[16] Zhang SX, Heng PA, Liu ZJ, Tan LW, Qiu MG, Li QY, Liao RX, Li K, Cui GY, Guo YL, Yang XP, Liu GJ, Shan JL, Liu JJ, Zhang WG, Chen XH, Chen JH, Wang J, Chen W, Lu M, You J, Pang XL, Xiao H, Xie YM. Creation of the Chinese visible human data set. Anat Rec B New Anat2003; 275(1): 190-195

[17] Alschinger M, Maniak M, Stietz F, Vartanyan T, TrägerF. Application of metal nanoparticles in confocal laser scanning microscopy: improved resolution by optical field enhancement. Appl Phys B2003; 76: 771-774

[18] Denk W, Horstmann H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol2004; 2(11): e329

[19] Andersson M, Groseclose MR, Deutch AY, Caprioli RM. Imaging mass spectrometry of proteins and peptides: 3D volume reconstruction. Nat Methods2008; 5(1): 101-108

[20] Denk W, Strickler JH, Webb WW. Two-photon laser scanning fluorescence microscopy. Science1990; 248(4951): 73-76

[21] Helmchen F, Denk W. Deep tissue two-photon microscopy. Nat Methods2005; 2(12): 932-940

[22] Theer P, Hasan MT, Denk W. Two-photon imaging to a depth of 1000 microns in living brains by use of a Ti:Al₂O₃ regenerative amplifier. Opt Lett2003; 28(12): 1022-1024

[23] Williams BS, Doyle MD. An internet atlas of mouse development. Comput Med Imaging Graph1996; 20(6): 433-447

[24] Wang H, Merchant SN, Sorensen MS. A downloadable three-dimensional virtual model of the visible ear. ORL J Otorhinolaryngol Relat Spec2007; 69(2): 63-67

[25] Woodward JD, Maina JN. A 3D digital reconstruction of the components of the gas exchange tissue of the lung of the muscovy duck, Cairina moschata. J Anat2005; 206(5): 477-492

[26] Song WC, Hu KS, Kim HJ, Koh KS. A study of the secretion mechanism of the sebaceous gland using three-dimensional reconstruction to examine the morphological relationship between the sebaceous gland and the arrector pili muscle in the follicular unit. Br J Dermatol2007; 157(2): 325-330

[27] Song WC, Hwang WJ, Shin C, Koh KS. A new model for the morphology of the arrector pili muscle in the follicular unit based on three-dimensional reconstruction. J Anat2006; 208(5): 643-648

[28] Wu H, Jaeger M, Wang M, Li B, Zhang BG. Three-dimensional distribution of vessels, passage cells and lateral roots along the root axis of winter wheat (Triticum aestivum). Ann Bot (Lond)2011; 107(5): 843-853

[29] Yang F, Deng ZS, Fan QH. A method for fast automated microscope image stitching. Micron2013; 48: 17-25

[30] Jia J, Tang CK. Image stitching using structure deformation. IEEE Trans Pattern Anal Mach Intell2008; 30(4): 617-631

[31] Zomet A, Levin A, Peleg S, Weiss Y. Seamless image stitching by minimizing false edges. IEEE Trans Image Process2006; 15(4): 969-977

[32] Paganelli C, Peroni M, Pennati F, Baroni G, Summers P, Bellomi M, Riboldi M. Scale Invariant Feature Transform as feature tracking method in 4D imaging: a feasibility study. Conf Proc IEEE Eng Med Biol Soc2012; 2012: 6543-6546

[33] Zito FA, Marzullo F, D’Errico D, Salvatore C, Digirolamo R, Labriola A, Pellecchia A. Quicktime virtual reality technology in light microscopy to support medical education in pathology. Mod Pathol2004; 17(6): 728-731

[34] Ma B, Zimmermann T, Rohde M, Winkelbach S, He F, Lindenmaier W, Dittmar KE. Use of Autostitch for automatic stitching of microscope images. Micron2007; 38(5): 492-499

[35] Kurien T, Boyce RW, Paish EC, Ronan J, Maddison J, Rakha EA, Green AR, Ellis IO. Three dimensional reconstruction of a human breast carcinoma using routine laboratory equipment and immunohistochemistry. J Clin Pathol2005; 58(9): 968-972

[36] Mai KT, Yazdi HM, Burns BF, Perkins DG. Pattern of distribution of intraductal and infiltrating ductal carcinoma: a three-dimensional study using serial coronal giant sections of the breast. Hum Pathol2000; 31(4): 464-474

[37] Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Phys Med Biol2001; 46(3): R1-R45

[38] Fernandez JJ. Computational methods for electron tomography. Micron2012; 43(10): 1010-1030

[39] Zaraga F, Langfelder G. White balance by tunable spectral responsivities. J Opt Soc Am A Opt Image Sci Vis2010; 27(1): 31-39

[40] Sibarita JB. Deconvolution microscopy. Adv Biochem Eng Biotechnol2005; 95: 201-243

[41] Zitová B, Flusser J. Image registration methods: a survey. Image Vis Comput2003; 21(11): 977-1000

[42] Lippolis G, Edsjö A, Helczynski L, Bjartell A, Overgaard NC. Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections. BMC Cancer2013; 13(1): 408

[43] Randell SH, Mercer RR, Young SL. Postnatal growth of pulmonary acini and alveoli in normal and oxygen-exposed rats studied by serial section reconstructions. Am J Anat1989; 186(1): 55-68

[44] Woodward JD, Maina JN. Study of the structure of the air and blood capillaries of the gas exchange tissue of the avian lung by serial section three-dimensional reconstruction. J Microsc2008; 230(1): 84-93

[45] Crum WR, Hartkens T, Hill DL. Non-rigid image registration: theory and practice. Br J Radiol2004; 77(Spec No. 2): S140-S153

[46] Christina Lee WC, Tublin ME, Chapman BE. Registration of MR and CT images of the liver: comparison of voxel similarity and surface based registration algorithms. Comput Methods Programs Biomed2005; 78(2): 101-114

[47] Arai TJ, Villongco CT, Villongco MT, Hopkins SR, Theilmann RJ. Affine transformation registers small scale lung deformation. Conf Proc IEEE Eng Med Biol Soc2012; 2012: 5298-5301

[48] Hong K, Hong J, Jung JH, Park JH, Lee B. Rectification of elemental image set and extraction of lens lattice by projective image transformation in integral imaging. Opt Express2010; 18(11): 12002-12016

[49] Ross JC, San José Estépar R, Kindlmann G, Díaz A, Westin CF, Silverman EK, Washko GR. Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation. Med Image Comput Comput Assist Interv2010; 13(Pt 3): 163-171

[50] Ma Z, Tavares JMRS, Jorge RN, Mascarenhas T. A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin2010; 13(2): 235-246

[51] Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng2000; 2(1): 315-337

[52] Le Pogam A, Hatt M, Descourt P, Boussion N, Tsoumpas C, Turkheimer FE, Prunier-Aesch C, Baulieu JL, Guilloteau D, Visvikis D. Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography. Med Phys2011; 38(9): 4920-4923

[53] Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell1986; 8(6): 679-698

[54] Pan Z, Lu J. A bayes-based region-growing algorithm for medical image segmentation. Comput Sci Eng2007; 9(4): 32-38

[55] Wang H, Chen X, Moss RH, Stanley RJ, Stoecker WV, Celebi ME, Szalapski TM, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW. Watershed segmentation of dermoscopy images using a watershed technique. Skin Res Technol2010; 16(3): 378-384

[56] Maksimovic R, Stankovic S, Milovanovic D. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models-‘snakes’. Int J Med Inform2000; 58-59: 29-37

[57] Molinari F1, Meiburger KM, Acharya UR, Zeng G, Rodrigues PS, Saba L, Nicolaides A, Suri JS. CARES 3.0: a two stage system combining feature-based recognition and edge-based segmentation for CIMT measurement on a multi-institutional ultrasound database of 300 images. Conf Proc IEEE Eng Med Biol Soc2011; 2011: 5149-5152

[58] Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys1993; 20(4): 1033-1048

[59] Zaidi H. Quantitative analysis in nuclear medicine imaging. 1st ed. New York, NY: Springer, 2005

[60] Choplin RH1, Farber JM, Buckwalter KA, Swan S. Three-dimensional volume rendering of the tendons of the ankle and foot. Semin Musculoskelet Radiol2004; 8(2): 175-183

[61] Tam MDBS. Building virtual models by postprocessing radiology images: A guide for anatomy faculty. Anat Sci Educ2010; 3(5): 261-266

[62] Clendenon JL, Byars JM, Hyink DP. Image processing software for 3D light microscopy. Nephron, Exp Nephrol2006; 103(2): e50-e54

[63] Wu X, Yu Z, Liu N. Comparison of approaches for microscopic imaging of skin lymphatic vessels. Scanning2012; 34(3): 174-180

[64] Sun K, Zhang J, Chen T, Chen Z, Chen Z, Li Z, Li H, Hu P. Three-dimensional reconstruction and visualization of the median nerve from serial tissue sections. Microsurgery2009; 29(7): 573-577

[65] Teutsch HF, Schuerfeld D, Groezinger E. Three-dimensional reconstruction of parenchymal units in the liver of the rat. Hepatology1999; 29(2): 494-505

[66] Helmstaedter M, Mitra PP. Computational methods and challenges for large-scale circuit mapping. Curr Opin Neurobiol2012; 22(1): 162-169

[67] Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature2013; 500(7461): 168-174

[68] Helmstaedter M, Briggman KL, Denk W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci2011; 14(8): 1081-1088

[69] Ewald AJ, McBride H, Reddington M, Fraser SE, Kerschmann R. Surface imaging microscopy, an automated method for visualizing whole embryo samples in three dimensions at high resolution. Dev Dyn2002; 225(3): 369-375

[70] Weninger WJ, Mohun T. Phenotyping transgenic embryos: a rapid 3-D screening method based on episcopic fluorescence image capturing. Nat Genet2002; 30(1): 59-65

[71] Blumer MJ, Gahleitner P, Narzt T, Handl C, Ruthensteiner B. Ribbons of semithin sections: an advanced method with a new type of diamond knife. J Neurosci Methods2002; 120(1): 11-16

[72] Chen SG, Tzeng YS, Wang CH. Treatment of severe burn with DermACELL^®, an acellular dermal matrix. Int J Burns Trauma2012; 2(2): 105-109

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