Please wait a minute...
Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2015, Vol. 9 Issue (1) : 30-45     DOI: 10.1007/s11684-014-0337-z
Three-dimensional reconstruction of light microscopy image sections: present and future
Yuzhen Wang,Rui Xu,Gaoxing Luo,Jun Wu()
Institute of Burn Research, Southwest Hospital, State Key Laboratory of Trauma, Burns, and Combined Injury, Chongqing Key Laboratory for Diseases Proteomics, the Third Military Medical University, Chongqing 400038, China
Download: PDF(3126 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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     
Corresponding Authors: Jun Wu   
Online First Date: 19 June 2014    Issue Date: 02 March 2015
URL:     OR
XY resolution Application Advantages Drawbacks References
X-ray microtomography (microCT) 300–700 nm In vivo analysis and 3D imaging (embryos, bone, tumor, stomatology, granular, and porous materials) Non-destructive;different perspective imaging No ideal methods for specific staining [2,5,6]
Microscopic magnetic resonance imaging (microMRI) Approximately 10 μm 1. In vivo monitoring and 3D imaging Non-destructive; Limitations of physical resolution [2,7]
2. Untreated or living biological specimens can also be used higher soft tissue contrast
Orthogonal-plane fluorescence optical sectioning (OPFOS) microscopy 20 μm 1. Ideal for the analysis of the intact mammalian cochlea 1. Different series of 2D images from the same specimen can be acquired 1. Inevitable damage to tissue microstructures [4,8,9]
2. Limited resolution
2. Quantitative measurements can be obtained 2. Less time-consuming 3. Not suitable for in vivo studies
Optical projection tomography (OPT) A resolution of microns to tens of μm 1. 3D visualization of soft tissue, cells, protein distribution, and gene expression patterns in biomedical specimens 1. Suitable for in vivo studies Difficult to control the distribution and provide sufficient fluorescent or colored stains [1012]
2. Suitable for 1–10 mm-thick specimens 2. Fills the imaging gap between MRI and confocal microscopy
Confocal laser scanning microscopy (CLSM) 0.5–1 μm Cell behavior, nerve endings, distribution of biomolecules, analysis of 3D pore structures, and other biomechanical parameters in porous nano/microfibrous biomaterials Suitable for non-invasive in vivo detection and quantification 1. Maximum specimen thickness is approximately 100 μm [2,1315]
2. Distribution and sufficient fluorescent markers
3D reconstruction of serial physical sections Sub-micron resolution Almost all tissue ranging from bones to soft tissue 1. High resolution and large specimen size 1. Comparatively laborious [16]
2. Dyeing of the target components is easy to control 2. Not suitable for in vivo studies
Tab.1  Descriptions and comparisons of several 3D imaging and visualization techniques
Fig.1  HE-stained skin sections with different fiducial markers. A V-shaped groove made by cutting part of the epidermis (A) and mouse sciatic nerves (B). A surgical cotton suture (C) and medical absorbable sutures (D) were used as vertical registration markers through skin specimens. We can clearly observe man-made structural deformations (A, B) and missing suture fibers (C, D). The shapes of the markers vary dramatically between adjacent slices.
Fig.2  Flow diagram for image registration.

Step 1: Feature detection. Manual or automatic detection of salient and distinctive objects, such as edges, contours, line intersections, closed-boundary regions, and corners, etc.

Step 2: Feature matching. Establishing the correspondence between the features detected in both the sensed and reference images.

Step 3: Transform model estimation. Establishing the type and parameters of the mapping functions.

Step 4: Image resampling and transformation. Transforming the sensed image using the mapping functions.

Fig.3  Alignment of adjacent sections from prostatic tissue. Left: HE; Right: p63/AMACR. The arrows link the matching inliers on the two images after rotation and scaling of the right image. In a perfect alignment, the arrows would be parallel. However, this expectation is unrealistic in practice. Cited from Lippolis et al.’s Ref. [42], with permission from BioMed Central.
Type of transformation Distance between any two pixels in the image Methods Limitations
Rigid registration Rotation and translation Unchanged Head-and-hat algorithm [46] Not suitable for non-rigid or nonlinear objects, which are common in the human body
Non-rigid registration Affine [47], projective [48], and curved transformation Changed Based on spatial transformation (Thin Plate Splines [49], Basis Function); Comparatively time-consuming, not as robust, and fewer evaluation methods
Based on pseudophysical models (Elastic Model [45], Viscous Fluid Model [50], Optical Flow Model)
Tab.2  Comparison of rigid registration and non-rigid registration
Fig.4  Transverse section of the follicular unit at the largest dimension of the sebaceous gland. Blue margin, sebaceous lobules located in the angular position; yellow margin, sebaceous lobules located in the counter-angular position. APM, arrector pili muscle; HF, hair follicle; SG, sebaceous gland. Masson’s trichrome stain. Scale bar= 200 μm. Cited from Song et al.’s Ref. [26], with permission from John Wiley and Sons.
Segmentation algorithm Classification Applications
Based on thresholds Edge-based algorithms Wavelet transform, Canny edge detection [53], Sobel edge detection, and Laplacian edge detection
Region-based algorithms Region growing algorithms [54]
Hybrid algorithms Watershed algorithms [55]
Based on clustering techniques Supervised classification algorithms K-nearest neighbor classifiers, maximum likelihood algorithms, supervised artificial neural network, support vector machine, active shape model, and active appearance model
Unsupervised classification algorithms CM algorithms, fuzzy C-means (FCM) algorithms, iterative self-organizing data analysis technique algorithm, and unsupervised neural networks
Based on deformable models Parametric deformable models Snake method [56]
Geometric deformable models Geodesic active contour algorithm
Tab.3  Algorithms for medical image segmentation
Applications Organ/tissue Thickness/ number of sections Fixation Stain method Displayed components Software
1 Human scalp skin 6 μm/200–350 10% neutral buffered formalin Masson’s trichrome Arrector pili muscles, hair follicles, sebaceous glands “Reconstruct” software
2 Human lower limb skin 5 μm/5 4% paraformaldehyde Podoplanin Immunohistochemistry Lymphatic vessels Amira Version 5.0
3 Human median nerve 20 μm/4650 Liquid nitrogen Acetylcholinesterase histochemical staining Motor nerve fibers and sensory nerve fibers Adobe Photoshop CS2 software; 3D nerve visualization system (China)
4 Rat liver 15 μm/146 Liquid nitrogen Glucose-6-phosphatase (G6Pase)histochemical staining Glucose-6-phosphatase (G6Pase) Histol (Germany)
5 Duck lung 0.3 μm/194 2.5% glutaraldehyde solution Toluidene blue stain Air capillaries (ACs) and blood capillaries (BCs) Corel Photopaint V.08? (Corel Corporation)
Tab.4  Applications of 3D reconstruction
Fig.5  Reconstructed three-hair follicular units. (a1, b1, and c1) Lateral views; (a2, b2, and c2) superior oblique views; (a3, b3, and c3) superior views. Blue, hair follicles; red, arrector pili muscle; yellow, sebaceous gland. Cited from Song et al.’s Ref. [26], with permission from John Wiley and Sons.
Fig.6  Reconstructions of a 3D lymphatic capillary network of human lower limb skin specimens using five individually stained 5 mm serial sections with podoplanin. The reconstructed lymphatic vessels are shown in yellow. Cited from Wu et al.’s Ref. [63], with permission from John Wiley and Sons.
Fig.7  (A) Transection of the median nerve at the place where the median nerve passed through the pronator teres. Among the 17 fascicles, six motor fascicles, six sensory fascicles, and five mixed fascicles can be found. (B) 3D visualization of a segment of the median nerve at this site. The direction of the arrow is from proximal to distal of the median nerve. Red represents the sensory fascicles; yellow represents the motor fascicles; and green represents the mixed fascicles. Cited from Sun et al.’s Ref. [64], with permission from John Wiley and Sons.
Fig.8  3D reconstruction of the secondary parenchymal unit and draining central venular tree. (A) Front view of the secondary unit. (B) Back view. Numerals indicate the primary units that are color-coded. Portal tracts are marked in red (p1, p2, and p3). (C) Front view of the draining central venular tree. (D) Back view. Numerals indicate the different central venular branches that are color-coded to correspond to the primary units. The stem of the central venular tree empties into a sublobular vein (sv). Cited from Teutsch et al.’s Ref. [65], with permission from John Wiley and Sons.
Fig.9  (A, B) Integrated (combined) reconstruction of the blood vessels (red) and airways (blue) (A) and that of the vasculature (B): the area of the reconstruction is close to the parabronchial lumen. Labels are similar to show the topographic locations of the same structures. AC, air capillary; BC, blood capillary; If, infundibulae; stars, points at which ACs interconnect. Evidently, the ACs and BCs are not mirror images. Scale bar, 10 μm. (C, D) Structure of the ACs. The ACs are heterogeneous, rather rotund structures that anastomose openly or via narrow tubules (stars). The spaces between the ACs are almost completely filled by BCs. If, infundibulum. (D) An AC and interconnection (star) are delineated with dashed lines. Scale bars: (C) 20 μm; (D) 10 μm. Cited from Woodward et al.’s Ref.[25], with permission from John Wiley and Sons.
1 Dickinson ME. Multimodal imaging of mouse development: tools for the postgenomic era. Dev Dyn 2006; 235(9): 2386-2400
doi: 10.1002/dvdy.20889 pmid: 16871621
2 Handschuh S, Schwaha T, Metscher BD. Showing their true colors: a practical approach to volume rendering from serial sections. BMC Dev Biol 2010; 10(1): 41
doi: 10.1186/1471-213X-10-41 pmid: 20409315
3 Liu B, Gao XL, Yin HX, Luo SQ, Lu J. A detailed 3D model of the guinea pig cochlea. Brain Struct Funct 2007; 212(2): 223-230
doi: 10.1007/s00429-007-0146-0 pmid: 17717692
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 Engin 2011; 14(7): 581-594
doi: 10.1080/10255842.2010.487049 pmid: 21082462
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 Pharm 2013; 445(1-2): 125-133
doi: 10.1016/j.ijpharm.2013.02.010 pmid: 23402981
6 Metscher BD. MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiol 2009; 9(1): 11
doi: 10.1186/1472-6793-9-11 pmid: 19545439
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. Europace 2012; 14(Suppl 5): v73-v81
doi: 10.1093/europace/eus276 pmid: 23104918
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 Microsc 2009; 233(2): 251-257
doi: 10.1111/j.1365-2818.2009.03115.x pmid: 19220691
9 Voie AH, Burns DH, Spelman FA. Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens. J Microsc 1993; 170(3): 229-236
doi: 10.1111/j.1365-2818.1993.tb03346.x pmid: 8371260
10 Sharpe J. Optical projection tomography. Annu Rev Biomed Eng 2004; 6(1): 209-228
doi: 10.1146/annurev.bioeng.6.040803.140210 pmid: 15255768
11 Eriksson AU, Svensson C, H?rnblad 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 Exp 2013; (71): e50238
pmid: 23353681
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 ONE 2012; 7(4): e34427
doi: 10.1371/journal.pone.0034427 pmid: 22509302
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 Chem 2013; 85(7): 3723-3729
doi: 10.1021/ac400076p pmid: 23452251
14 Hu W, Lux R, Shi W. Analysis of exopolysaccharides in Myxococcus xanthus using confocal laser scanning microscopy. Methods Mol Biol 2013; 966: 121-131
doi: 10.1007/978-1-62703-245-2_8 pmid: 23299732
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 Zasshi 2012; 132(12): 1347-1354 (in Japanese)
doi: 10.1248/yakushi.12-00234-1 pmid: 23208040
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 Anat 2003; 275(1): 190-195
doi: 10.1002/ar.b.10035 pmid: 14628319
17 Alschinger M, Maniak M, Stietz F, Vartanyan T, Tr?ger F. Application of metal nanoparticles in confocal laser scanning microscopy: improved resolution by optical field enhancement. Appl Phys B 2003; 76: 771-774
doi: 10.1007/s00340-003-1182-y
18 Denk W, Horstmann H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol 2004; 2(11): e329
doi: 10.1371/journal.pbio.0020329 pmid: 15514700
19 Andersson M, Groseclose MR, Deutch AY, Caprioli RM. Imaging mass spectrometry of proteins and peptides: 3D volume reconstruction. Nat Methods 2008; 5(1): 101-108
doi: 10.1038/nmeth1145 pmid: 18165806
20 Denk W, Strickler JH, Webb WW. Two-photon laser scanning fluorescence microscopy. Science 1990; 248(4951): 73-76
doi: 10.1126/science.2321027 pmid: 2321027
21 Helmchen F, Denk W. Deep tissue two-photon microscopy. Nat Methods 2005; 2(12): 932-940
doi: 10.1038/nmeth818 pmid: 16299478
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 Lett 2003; 28(12): 1022-1024
doi: 10.1364/OL.28.001022 pmid: 12836766
23 Williams BS, Doyle MD. An internet atlas of mouse development. Comput Med Imaging Graph 1996; 20(6): 433-447
doi: 10.1016/S0895-6111(96)00041-9 pmid: 9007211
24 Wang H, Merchant SN, Sorensen MS. A downloadable three-dimensional virtual model of the visible ear. ORL J Otorhinolaryngol Relat Spec 2007; 69(2): 63-67
doi: 10.1159/000097369 pmid: 17124433
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 Anat 2005; 206(5): 477-492
doi: 10.1111/j.1469-7580.2005.00413.x pmid: 15857367
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 Dermatol 2007; 157(2): 325-330
doi: 10.1111/j.1365-2133.2007.08036.x pmid: 17596168
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 Anat 2006; 208(5): 643-648
doi: 10.1111/j.1469-7580.2006.00575.x pmid: 16637886
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
doi: 10.1093/aob/mcr005 pmid: 21289027
29 Yang F, Deng ZS, Fan QH. A method for fast automated microscope image stitching. Micron 2013; 48: 17-25
doi: 10.1016/j.micron.2013.01.006 pmid: 23465523
30 Jia J, Tang CK. Image stitching using structure deformation. IEEE Trans Pattern Anal Mach Intell 2008; 30(4): 617-631
doi: 10.1109/TPAMI.2007.70729 pmid: 18276968
31 Zomet A, Levin A, Peleg S, Weiss Y. Seamless image stitching by minimizing false edges. IEEE Trans Image Process 2006; 15(4): 969-977
doi: 10.1109/TIP.2005.863958 pmid: 16579382
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 Soc 2012; 2012: 6543-6546
pmid: 23367428
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 Pathol 2004; 17(6): 728-731
doi: 10.1038/modpathol.3800113 pmid: 15073600
34 Ma B, Zimmermann T, Rohde M, Winkelbach S, He F, Lindenmaier W, Dittmar KE. Use of Autostitch for automatic stitching of microscope images. Micron 2007; 38(5): 492-499
doi: 10.1016/j.micron.2006.07.027 pmid: 17045805
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 Pathol 2005; 58(9): 968-972
doi: 10.1136/jcp.2004.024794 pmid: 16126880
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 Pathol 2000; 31(4): 464-474
doi: 10.1053/hp.2000.6536 pmid: 10821494
37 Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Phys Med Biol 2001; 46(3): R1-R45
doi: 10.1088/0031-9155/46/3/201 pmid: 11277237
38 Fernandez JJ. Computational methods for electron tomography. Micron 2012; 43(10): 1010-1030
doi: 10.1016/j.micron.2012.05.003 pmid: 22658288
39 Zaraga F, Langfelder G. White balance by tunable spectral responsivities. J Opt Soc Am A Opt Image Sci Vis 2010; 27(1): 31-39
doi: 10.1364/JOSAA.27.000031 pmid: 20035300
40 Sibarita JB. Deconvolution microscopy. Adv Biochem Eng Biotechnol 2005; 95: 201-243
doi: 10.1007/b102215 pmid: 16080270
41 Zitová B, Flusser J. Image registration methods: a survey. Image Vis Comput 2003; 21(11): 977-1000
doi: 10.1016/S0262-8856(03)00137-9
42 Lippolis G, Edsj? A, Helczynski L, Bjartell A, Overgaard NC. Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections. BMC Cancer 2013; 13(1): 408
doi: 10.1186/1471-2407-13-408 pmid: 24010502
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 Anat 1989; 186(1): 55-68
doi: 10.1002/aja.1001860105 pmid: 2782288
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 Microsc 2008; 230(1): 84-93
doi: 10.1111/j.1365-2818.2008.01958.x pmid: 18387043
45 Crum WR, Hartkens T, Hill DL. Non-rigid image registration: theory and practice. Br J Radiol 2004; 77(Spec No. 2): S140-S153
doi: 10.1259/bjr/25329214 pmid: 15677356
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 Biomed 2005; 78(2): 101-114
doi: 10.1016/j.cmpb.2004.12.006 pmid: 15848266
47 Arai TJ, Villongco CT, Villongco MT, Hopkins SR, Theilmann RJ. Affine transformation registers small scale lung deformation. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 5298-5301
pmid: 23367125
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 Express 2010; 18(11): 12002-12016
doi: 10.1364/OE.18.012002 pmid: 20589062
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 Interv 2010; 13(Pt 3): 163-171
pmid: 20879396
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 Engin 2010; 13(2): 235-246
doi: 10.1080/10255840903131878 pmid: 19657801
51 Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000; 2(1): 315-337
doi: 10.1146/annurev.bioeng.2.1.315 pmid: 11701515
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 Phys 2011; 38(9): 4920-4923
doi: 10.1118/1.3608907 pmid: 21978037
53 Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679-698
doi: 10.1109/TPAMI.1986.4767851 pmid: 21869365
54 Pan Z, Lu J. A bayes-based region-growing algorithm for medical image segmentation. Comput Sci Eng 2007; 9(4): 32-38
doi: 10.1109/MCSE.2007.67
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 Technol 2010; 16(3): 378-384
pmid: 20637008
56 Maksimovic R, Stankovic S, Milovanovic D. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models-‘snakes’. Int J Med Inform 2000; 58-59: 29-37
doi: 10.1016/S1386-5056(00)00073-3 pmid: 10978907
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 Soc 2011; 2011: 5149-5152
pmid: 22255498
58 Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys 1993; 20(4): 1033-1048
doi: 10.1118/1.597000 pmid: 8413011
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 Radiol 2004; 8(2): 175-183
pmid: 15195236
61 Tam MDBS. Building virtual models by postprocessing radiology images: A guide for anatomy faculty. Anat Sci Educ 2010; 3(5): 261-266
doi: 10.1002/ase.175 pmid: 20827725
62 Clendenon JL, Byars JM, Hyink DP. Image processing software for 3D light microscopy. Nephron, Exp Nephrol 2006; 103(2): e50-e54
doi: 10.1159/000090616 pmid: 16543764
63 Wu X, Yu Z, Liu N. Comparison of approaches for microscopic imaging of skin lymphatic vessels. Scanning 2012; 34(3): 174-180
doi: 10.1002/sca.20285 pmid: 21898460
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. Microsurgery 2009; 29(7): 573-577
doi: 10.1002/micr.20646 pmid: 19308949
65 Teutsch HF, Schuerfeld D, Groezinger E. Three-dimensional reconstruction of parenchymal units in the liver of the rat. Hepatology 1999; 29(2): 494-505
doi: 10.1002/hep.510290243 pmid: 9918927
66 Helmstaedter M, Mitra PP. Computational methods and challenges for large-scale circuit mapping. Curr Opin Neurobiol 2012; 22(1): 162-169
doi: 10.1016/j.conb.2011.11.010 pmid: 22221862
67 Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 2013; 500(7461): 168-174
doi: 10.1038/nature12346 pmid: 23925239
68 Helmstaedter M, Briggman KL, Denk W. High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 2011; 14(8): 1081-1088
doi: 10.1038/nn.2868 pmid: 21743472
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 Dyn 2002; 225(3): 369-375
doi: 10.1002/dvdy.10169 pmid: 12412023
70 Weninger WJ, Mohun T. Phenotyping transgenic embryos: a rapid 3-D screening method based on episcopic fluorescence image capturing. Nat Genet 2002; 30(1): 59-65
doi: 10.1038/ng785 pmid: 11743576
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 Methods 2002; 120(1): 11-16
doi: 10.1016/S0165-0270(02)00166-8 pmid: 12351202
72 Chen SG, Tzeng YS, Wang CH. Treatment of severe burn with DermACELL?, an acellular dermal matrix. Int J Burns Trauma 2012; 2(2): 105-109
pmid: 23071908
No related articles found!
Full text