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s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x

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journal homepage: www.elsevier .com/locate/survophthal

Diagnostic and surgical techniques

Quantitative analysis of in vivo confocal microscopy images:A review

Dipika V. Patel, PhD, MRCOphth*, Charles N. McGhee, PhD, FRCOphth

Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland,

New Zealand

a r t i c l e i n f o

Article history:

Received 2 August 2012

Received in revised form

9 December 2012

Accepted 11 December 2012

Neelakshi Bhagat and David Chu,

Editors

Keywords:

In vivo confocal microscopy

cornea

quantitative analysis

repeatability

corneal endothelium

corneal epithelium

keratocytes

sub-basal nerves

corneal thickness

* Corresponding author: Dipika V Patel, PhMedical and Health Sciences, University of A

E-mail address: dipika.patel@auckland.ac0039-6257/$ e see front matter ª 2013 Elsevhttp://dx.doi.org/10.1016/j.survophthal.2012.

a b s t r a c t

In vivo confocal microscopy (IVCM) is a non-invasive method of examining the living

human cornea. The recent trend towards quantitative studies using IVCM has led to the

development of a variety of methods for quantifying image parameters. When selecting

IVCM images for quantitative analysis, it is important to be consistent regarding the

location, depth, and quality of images. All images should be de-identified, randomized, and

calibrated prior to analysis. Numerous image analysis software are available, each with

their own advantages and disadvantages.

Criteria for analyzing corneal epithelium, sub-basal nerves, keratocytes, endothelium,

and immune/inflammatory cells have been developed, although there is inconsistency

among research groups regarding parameter definition. The quantification of stromal nerve

parameters, however, remains a challenge. Most studies report lower inter-observer

repeatability compared with intra-observer repeatability, and observer experience is

known to be an important factor. Standardization of IVCM image analysis through the use of

a reading centerwould be crucial for any future large,multi-centre clinical trials using IVCM.

ª 2013 Elsevier Inc. All rights reserved.

In vivo confocal microscopy (IVCM) is a noninvasive method

quantification is crucial for objectively assessing the effects of

of examining the living human cornea under high magnifi-

cation in healthy and pathological states. These attributes

make it a powerful clinical and research tool.45 Themajority of

early studies using this technique have been qualitative in

nature. For example, the diagnosis of infectious keratitis

typically requires qualitative analysis of images by an expe-

rienced observer, and no quantitative studies are currently

available. The recent trend towards quantitative studies using

IVCM has led to the development of a variety of methods for

quantifying image parameters. As well as establishing

the normal range of cell densities in healthy corneas,

D, MRCOphth, Departmenuckland, Private Bag 920.nz (D. V. Patel).ier Inc. All rights reserve12.003

pathology or therapeutic interventions on these parameters.

We highlight IVCM image parameters that may be quan-

tified, including discussion of analysis techniques, limita-

tions, and repeatability.

1. Image selection and analysis

When selecting IVCM images for quantitative analysis, it is

important to be consistent regarding the location (central vs

peripheral cornea) and depth of images. Consistency in

t of Ophthalmology, New Zealand National Eye Centre, Faculty of19, Auckland,New Zealand. 1142.

d.

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x2

corneal location may be maximized by the use of fixation

targets–internal for slit-scanning IVCM17 or external for laser

scanning IVCM.47 Accuracy in determining section depth can

be maximized by using fixed landmarks (e.g. measuring ker-

atocyte density immediately posterior to Bowman’s layer) or

by using devices such as the “z-ring encoder” (see the Corneal

Thickness section).

Currently, there is no consensus regarding the minimum

number of images required for representative quantitative

analysis, although a single image is generally considered

insufficient. Themajority of published studies have used up to

five images per layer per eye.

The quality of the selected images is key. Obviously,

blurred or non-tangential images should be excluded

(Fig. 1),34 and once image selection has been completed, all

images should be de-identified and randomized by an

independent investigator prior to analysis to avoid

observer bias.

All IVCM images must be appropriately calibrated, but

the parameters will vary depending on the type of micro-

scope used. The standardization and choice of frame size

are important factors given the differing contrast distribu-

tion across images from different types of IVCM. In partic-

ular, slit scanning IVCM images exhibit decreased contrast

towards the lateral edges of the image, and thus fewer of the

measured structures may be visualized in these regions.43,48

In such cases, therefore, restricting the analysis frame to the

central, higher contrast region of the image may be appro-

priate. When analyzing cell densities, to standardize meth-

odology the majority of published studies exclude all cells

that overlap two predefined borders of the selected

frame.19,26,32

2. Image analysis software

2.1. Proprietary software

2.1.1. Nidek Advanced Vision Information System softwareNidek Advanced Vision Information System (NAVIS) Endo-

thelial Analysis Software (Fig. 2), available for use with the

Fig. 1 e Laser scanning in vivo confocal microscopy images sho

involuntary movements of the patient’s eye at the time of imagi

stroma, sub-basal nerve plexus, and basal epithelium.

ConfoScan IVCM software (Nidek Technologies, Fremont, CA)

enables quantitative analysis of in vivo confocal images. The

region of interest is easily defined in terms of area, the

dimensions of which may be adjusted as required prior to

analysis. For corneal endothelial images, analysis may be

performed manually, automatically, or using a combination

of both techniques. Manual analysis only provides data

regarding endothelial cell density, whereas automatic anal-

ysis of endothelial images has the advantage of providing data

regarding endothelial density (and the normal range for the

subject’s age), the mean cell area, the coefficient of variation

in area, the mean number of sides, the coefficient of variation

in the number of sides, and the percentage of hexagonal cells.

In some cases, however, cell bordersmay be incorrectly traced

by the automated system. This can be rectified by manual

adjustment of cell border tracings. Lengths and areas of

objects larger than 1 mm may also be measured using this

software.

2.1.2. Rostock Corneal Module proprietary softwareThe Heidelberg Retina Tomograph II Rostock Corneal

Module (RCM; Heidelberg Engineering, GmBH, Germany) has

proprietary software for manual analysis of cell densities

(Fig. 3). Although the region of interest is easily defined, the

area of this region is not displayed until after selection,

making it difficult to select a region with a fixed area.

Additionally, there is no facility for automated image anal-

ysis or for making linear measurements.

2.2. Other commercially available software for IVCMimage analysis

A wide range of software is available for quantitative anal-

ysis of biological images. Commonly used software in IVCM

studies include: Image J (National Institutes of Health,

Bethesda, MD),25,55 a free public domain open source soft-

ware; Adobe Photoshop (Adobe Systems Inc, San Jose,

CA)28,36; AnalySIS (Soft Imaging System GmBH, Munster,

Germany)37,46; and AMIRA (Visage Imaging GmbH, Berlin,

Germany).62

wing (A) blurring and distortion of keratocyte nuclei due to

ng, and (B) an oblique optical section of the anterior corneal

Fig. 2 e User interface for Nidek Advanced Vision Information System software (NAVIS).

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x 3

3. Corneal epithelium

The corneal epithelium typically consists of five to seven

layers of cells, including superficial epithelial cells, wing cells,

and basal epithelial cells. Superficial cells have most

commonly been quantified in terms of cell diameter (mm) and

cell density (cells/mm2); images obtained by IVCMmay not be

sufficient to demonstrate full detail, however.39 Mocan et al39

have objectively shown that topical fluorescein application

prior to IVCM enhances the visualization of the superficial

epithelium, thus enablingmore accurate quantification of this

layer. It is therefore important to ensure consistency

regarding the use of fluorescein prior to IVCM imaging.

Wing cells may be quantified in terms of cell diameter (mm)

and cell density (cells/mm2). Whereas the superficial cell layer

is one to two cells thick and the basal cells form a monolayer,

thewing cell layer is three to six cells thick and has a lower cell

Fig. 3 e User interface for Rostock Corneal Module proprieta

density than that of the basal epithelium.7 It is therefore

important to distinguish wing cell layer images from the

similar basal cell layer.

Basal epithelial cells are the most easily and reproducibly

imaged and quantified of the epithelial layers as the result of

their location immediately anterior to Bowman layer and their

monolayer configuration. The majority of published studies

performed computer assisted manual analysis of cell diam-

eter (mm) and cell density (cells/mm2).1,3,7,44 Harrison et al12

suggested using the NAVIS automated endothelial analysis

software to analyze ConfoScan images of the basal epithe-

lium. This technique involves first inverting the image, then

performing automated analysis. Adjustment of the image

brightness and contrast is often required to aid the detection

of cell borders. The software is usually only able to accurately

identify 50% of basal epithelial cells, so extensive manual

adjustment is necessary. Unlike corneal endothelial cells,

basal epithelial cells have a more rounded appearance;

ry software for manual analysis of endothelial density.

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x4

therefore, although the software will automatically count the

number of sides for each cell, this feature is not accurate or

reliable for basal epithelial cells.12 The intra-examiner

repeatability of computer-assisted manual analysis of basal

cell density of RCM images is good, with an intra-class corre-

lation coefficient (ICC) of 0.89.3

4. Corneal sub-basal nerves

Corneal sub-basal nerves are easily and reproducibly imaged

because of their location and orientation on Bowman’s layer.

Nonetheless, the manner in which nerve density is defined

has been somewhat inconsistent in the literature. The

majority of studies have defined sub-basal nerve density as

the total length of nerves visible within a defined area (mm/

mm2 or mm/mm2),44,48 but some investigators have only

included nerve branches longer than 50 mm in their

measurements.9 Others have analyzed the total number of

nerves within a frame; the definitions for this vary, however,

from the number of long nerve fibre bundles57 to the sum of

nerve branches present.42 These differences in analysis make

reliable comparisons between studies difficult.

Efron et al8 reported high intra- and interexaminer

repeatability in nerve fibre length (NFL) measurement from

ConfoScan images in eyes with diabetes mellitus; the stan-

dard deviation was large (up to 2.33 mm/mm2), however.

Interestingly, the overall spread of data was smaller for the

observer who was more experienced at analyzing nerve fiber

images and for the second measurement. The reason why

both observers tended to assign a lower NFL value on the

second measurement than the first occasion is unclear, but

may indicate a shift in criteria toward a more conservative

approach when identifying nerve fibers.8

Other studies using slit-scanning IVCM images have also

shown good intra- and interoperator reproducibility in NFL

measures (intraclass correlation [ICC] ¼ 0.96, and ICC ¼ 0.94,

respectively).35 Grupcheva et al11 reported 93% intra-observer

repeatability and an inter-observer variation within 12% for

nerve density measurement. The latter is of similar magni-

tude to the variation (10%) noted by Benitez del Castillo et al.1

Investigations using the RCM have demonstrated excellent

intra-observer reliability (ICC ¼ 0.86) and very good inter-

observer reliability (0.74) for corneal nerve fibre density for

single images. For a set of 40 images, however, although intra-

observer reliability was excellent (ICC ¼ 0.83), interobserver

reliability was only moderate to very good (ICC ¼ 0.60). For the

more clinically relevant analysis of repeated patient exami-

nations (“study-level reproducibility”) intra-observer reli-

ability (ICC ¼ 0.57) and inter-observer reliability (ICC ¼ 0.61)

were only moderate.14 Intra-observer ICC for the number of

sub-basal nerves may be high (0.9) for RCM images.3,5

Semi-automated analysis of sub-basal nerves is both labor

intensive and subjective, requiring the investigator to manu-

ally trace all visible nerves. Scarpa et al59 made the first step in

addressing this issue with the development of a fully auto-

mated algorithm for analyzing sub-basal nerve length. The

average percentage of correctly recognized nerves length,

with respect to completely manually traced lengths of visible

nerves, was 80.4% in normal corneas and 83.8% on abnormal

corneas. The average rate of false nerve length recognition

using the automated software was 6.5% in normal corneas

and 9.1% in abnormal corneas. Other researchers have since

developed automated methods for detecting nerves in IVCM

images.

Prior to quantitative analysis, preprocessing of the raw

IVCM images is usually required to distinguish nerves from

background data, and this may be achieved using segmenta-

tion and skeletonization algorithms.18,69 Dabbah et al4 re-

ported a coefficient of variation for manual analysis of RCM

images of 0.34 compared with 0.29 for automated analysis

using a dual-model detection algorithm. Ferreira et al10 used

phase symmetry techniques in their algorithm to aid the

identification the linear structure of corneal nerves while

rejecting the regular oval shape of stromal keratocytes nuclei

appearing in slit-scanning IVCM images of the sub-basal nerve

plexus. Their method segmented corneal nerves with a

sensitivity near 90% and an average of 5.3% false recognitions.

Currently, there is no commercially available software for

automated analysis of IVCM images.

Formeasurements of the diameter of thin, highly reflective

structures (such as sub-basal nerves) or beading frequency to

be comparable, all images need to be acquired using a fixed

illumination intensity because illumination intensity affects

the apparent thickness of corneal nervesdparticularly as they

approach the limit of resolution. It follows that comparisons

of the dimensions of reflective objects in images obtained by

in vivo confocalmicroscopy are only validwhen the same type

of in vivo confocal microscope is used and illumination

intensity is constant.48 Interobserver variation in measuring

beading frequency has been reported to be 14%.1

Kallinikosetal22were thefirst todescribeanobjective, semi-

automated technique for quantifying sub-basal nerve tortu-

osity (tortuosity coefficient); the degree of agreement with

subjective grading had not been assessed, however. Although

intra- and interobserver repeatability using this technique is

very good for single images or image sets,14 intra- and inter-

observer reliability for analysis of repeated patient examina-

tions (“study-level reproducibility”) is poor (ICC¼ 0.23 and0.29,

respectively).

Scarpa et al60 modified their algorithm for the automatic

recognition of corneal nerve structures59 to enable automated

tracing of nerves and also devised a new method for auto-

mated analysis of nerve tortuosity. They evaluated the reli-

ability of their new system by comparing it to subjective

tortuosity grading by an experienced observer and showed

that their method achieved the lowest number of incorrect

classifications and the best concordance coefficient and class

separation.

5. Corneal stroma

5.1. Stromal nerves

Quantitative analysis of stromal nerves imaged by IVCM

remains controversial. A wide range of values for stromal

nerve diameter have been reported, and this variation is due

to a number of factors. First, stromal nerves commonly

traverse obliquely relative to the en face section of IVCM

Fig. 5 e Laser scanning in vivo confocal microscopy image

of the corneal mid-stroma. Although nuclei with the

highest contrast and sharpest edges can be consistently

identified, those with lower contrast and blurred edges

(arrowhead ) will be inconsistently identified by different

observers.

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x 5

images. These nerves will therefore appear shorter than one

whose path is parallel to the plane of the image (Fig. 4). The

visible nerve length per frame area will also depend on the

axial resolution of themicroscope used (i.e., a greater length of

nerve will be visible with a thicker optical section). Therefore,

measuring the length of nerve per frame does not necessarily

relate to the true stromal nerve density.48 The quantification

of stromal nerve parameters remains a challenge.

5.2. Keratocytes

Each two-dimensional IVCM image frame actually repre-

sents a volume with z-depth equal to the optical section

thickness.51 Keratocyte density has therefore been expressed

as density per unit area or per unit volume. In order to

calculate the volumetric density of keratocytes, stereological

principles must be applied that take into account the optical

section thickness and the mean size of cells normal to the

counting plane.53 The depth of field must therefore be

known when estimating volumetric cell density and,

because of variations in design, the depth of field must be

measured on the particular microscope used to record the

images of the cells.32

Classification of the stromal depth is also an important

aspect of keratocyte density analysis. The anterior stroma is

often defined as the first image posterior to Bowman’s layer,

and the posterior stroma as the first layer anterior to Desce-

met’s membrane.26 Further subdivision of the stroma varies

between studies and may be described in terms of corneal

depth or as a percentage of total corneal thickness.30,34

Manual analysis of keratocyte density involves marking

each clearly defined cell or nucleus in a predefined rectangular

frame.53 Although nuclei with the highest contrast and

sharpest edges can be consistently identified, those with

lower contrast and blurred edges will be inconsistently

Fig. 4 e Laser scanning in vivo confocal microscopy image

of a corneal stromal nerve leaving the optical section

(arrows) due to its oblique orientation.

identified (Fig. 5).34 This method is therefore both time

consuming and subjective and is hindered by high intra- and

interobserver variation.30 Estimates of human keratocyte

density in healthy corneas vary over a wide range, from as low

as 17,384 cells/mm3 in the posterior stroma38 to as high as

39,442 cells/mm3 in the anterior stroma.52

One study using tandem scanning IVCM highlighted that

mean cell densities assessed manually three times in the

same frames differed from each other by as much as 3,160

cells/mm3.34 Similarly, manual analysis of ConfoScan IVCM

images compared to histological images of donor human

corneas showed a difference of 2,377 cells/mm3, with 95%

confidence of �10,742 to 5,989 cells/mm3.53

Multiple studies have reported the repeatability of manual

analysis of keratocyte density from RCM images. One showed

mean a difference between repeated measurements of kera-

tocyte cell density of 4,300 cells/mm3 (95% limits of

agreement � 20,900 cells/mm3),64 whereas another demon-

strated intra- and interexaminer coefficients of variation of

5.2% and 2.9%, respectively.26 The ICC for anterior stromal

keratocyte density has been reported to be excellent (0.9).3

Automated analysis of keratocyte density was first intro-

duced by Prydal et al.54 Patel et al51 subsequently used

a custom program integrated into an image analysis program

to quantify keratocyte density automatically from tandem

scanning IVCM images of rabbit corneas. The coefficients of

variation of repeated estimates of keratocyte density using

this method ranged from 0.032 to 0.075. The mean difference

between cell density estimated by IVCM and histology was

1,184 cells/mm3, and the 95% limits of agreement were �9,329

cells/mm3 to 11,699 cells/mm3. Interestingly, agreement was

better for the anterior than for the posterior stroma.

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x6

McLaren et al34 further developed and refined the auto-

mated algorithm described by Patel et al51 and tested it on

tandem scanning IVCM images. Cell densities determined by

the automated method were compared with densities deter-

mined by manually counting keratocyte nuclei in the same

images, in a population of patients who underwent laser in

situ keratomileusis. The mean difference was �363 � 5214

cells/mm3. The same group also developed a program that

used image-processing routines to identify stromal cell nuclei

in ConfoScan 4 IVCM images.30

The presence of stromal nerves complicates computer

analysis, making measurements unreliable.54 Additionally,

image-processing programs developed for a particular

microscope cannot be directly applied to images from other

microscopes because the optical properties of each micro-

scope uniquely affect the cell selection criteria of the

program.30 Despite the development of automated methods

for keratocyte density analysis, none have yet been used

consistently in clinical studies.

6. Corneal endothelium

Manual analysis of endothelial cell density simply involves

counting the number of cells within a predefined frame. This

traditional form of analysis only provides information

regarding the cell density and number of cells counted and

does not supply any morphometric data. In contrast,

contemporary software applications such as the NAVIS soft-

ware of the ConfoScan IVCM (see the Image Analysis Software

section) provide data regarding the number of cells counted,

cell density, age-matched normal range of endothelial

density, mean cell area, coefficient of variation in cell area,

mean number of sides per cell, coefficient of variation in

number of sides per cell, and percentage of hexagonal cells.

A fundamental issue with automated endothelial analysis

is a failure to identify correctly endothelial cell borders,23,24

leading to overestimation of endothelial density compared

with manual analysis.20 The accuracy of cell border delinea-

tionmay be checked by the operator, and any inaccuracies can

be corrected by manual adjustment of cell borders (semi-

automated image analysis). Possible adjustments include

fusing cells (changing cell vertices), dividing cells, and erasing

cells. Kitzman et al23 have demonstrated that after manual

correction of cell borders detected by the NAVIS automated

software, the endothelial density, coefficient of variation of

cell area, and percentage of hexagonal cells were not different

from those determined by the Corners method.23,50 The

authors therefore concluded that when the ConfoScan and its

proprietary program are used to determine cell density,

investigators must manually correct cell boundaries on the

images. Doughty et al6 reported that at least 75 cells should be

counted per image for an acceptable level of inter-subject

variance.

Although semi-automated analysis improves the accuracy

of quantification of endothelial parameters,23,61 it is time-

consuming and therefore usually impractical in the clinical

setting.

Whenmanual analysis using the RCMproprietary software

was compared with automated analysis of non-contact

specular microscopy images, endothelial density differed

significantly. The RCM method underestimated endothelial

density in eyes with low cell density and overestimated

endothelial density in eyes with high cell density, although

intra and inter-observer reproducibility was good.58 Conflict-

ing data from Rieth et al56 showed no significant differences

between the two groups. Comparisons between the RCM

method and the semi-automated NAVIS method in healthy

eyes showed RCM endothelial density was significantly higher

and the degree of overestimation increased with higher

densities.63

In eyes that have undergone corneal transplantation (full

thickness or deep anterior lamellar), NAVIS software yields

a modest to substantial overestimation of endothelial

density.21 The main reason is over-segmenting of endothelial

cells by the software, commonly due to the presence of bright

endothelial nuclei (Fig. 6).49 The NAVIS manual counting

method provides lower endothelial density estimates than

planimetry (a technique that involves printing of the endo-

thelial image file following careful visual inspection and

manual outlining of the cellecell borders within the region of

interest with a pen) resulting in an underestimation of cell

density.21 Therefore, in corneal transplantation the semi-

automated NAVIS method is probably the best for

quantification.

An alternative, although perhaps less convenient, method

of quantitative endothelial assessment involves importing

IVCM images into the KSS-400 image analysis program

(Konan, Inc, Torrance, CA). Image rescaling and appropriate

calibration are required prior to analysis. Four endothelial

analysis techniques are described: the center method, the

flex-center method, the Corners method, and the variable

frame method. Patel et al50 showed good agreement in endo-

thelial density between the first three methods, although

agreement in morphometric data was poor.

7. Corneal reflectivity

Measurement of corneal reflectivity is an objective method of

assessing corneal haze.40 Most investigators have expressed

corneal haze in terms of the specific units of image intensity

from the instrument used for measurement; however, the

image “brightness” of light back-scattered from corneal haze

can only be compared with brightnessmeasured at a different

time in longitudinal studies or across laboratories if the IVCM

instruments are standardized so that units that express haze

intensity are equivalent. Unfortunately, brightness expressed

as a digitized video signal is only useful for relative

measurements from the same camera and varies depending

on its settings. Measurements can be standardized in two

ways. First, image intensity should be expressed in terms that

are meaningful and can be compared across laboratories.

Second, measurements of haze must be adjusted for differ-

ences or variations in the brightness of the light source and

the sensitivity of the light detectordvariables that may

change over time.31

The basal cell layer (BCL) index was developed by Mor-

ishige et al41 for the quantitative evaluation of corneal

epithelial edema. ConfoScan images (obtained with a fixed

Fig. 6 e In the presence of bright endothelial nuclei, automated endothelial analysis using NAVIS software usually

overestimates endothelial density due to oversegmentation of cells. Subsequent manual adjustment of cell borders gives

greater accuracy.

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x 7

light intensity of 60% of maximum) were exported into their

own program, which works by automatically defining a 100-

pixel diameter circle in the center of the image, measuring

the 8-bit pixel intensity inside the circle, and summing all the

pixel intensities. The BCL index is defined as the total pixel

intensity divided by 1,000. The intra-observer repeatability for

this method was reported to be good with a coefficient of

variation (CoV) of 3.4%. Mocan et al39 evaluated epithelial

reflectivity using the Z-scan mode of the ConfoScan. In this

method, the intensity levels of the brightest epithelial layers

were recorded in terms of intensity units with a range of

0 to 255.

When measurements are standardized with a standard

turbidity suspension, corneal back-scatter measurements

(expressed in scatter units) of ConfoScan IVCM images had

intra-observer CoV of 4.8e6.8% and there was no difference

between intra- and inter-session repeatability.15 Good inter-

session repeatability (CoV ¼ 0.4e2.9%) is present for solid as

well as for liquid reference standards.16 The brightness of the

illuminating light source in the RCM is usually varied auto-

matically by the instrument in order to maximize image

quality. Measurement of reflectivity using these images is

therefore inappropriate.

8. Corneal thickness

The slit-scanning IVCM has poor repeatability for corneal

thickness measurements, exhibiting the widest 95% limits

of agreement both within and between sessions when

compared with ultrasound, optical coherence tomography,

and Orbscan.67

This is because the position of the cornea relative to the

objective lens varies throughout the scan acquisition, and the

error is compounded by the several second length of scanning

time during which involuntary axial motion of the eye is

inevitable.33

The relatively recently developed “z-ring” for the Con-

foScan may significantly improve the accuracy of thickness

measurements.32 The z-ring remains in contact with the

surface of the cornea during scanning to stabilize the eye and

allows the objective to move with the cornea in the ante-

rioreposterior direction. Using the graph of intensity of the

confocal images, corneal thickness is determined using the

distance between the brightest image of the surface epithe-

lium and the endothelium. Using polymethyl methacrylate

contact lenses of known thickness, measurements using the

ConfoScan 4 with the z-ring were accurate to within 5 mm.2,33

When imaging human corneas, the intra-instrument repro-

ducibility of corneal thickness measurements using this

technique was excellent (ICC ¼ 0.989). There was also no

significant difference in corneal thickness measurements

between scans taken by two different operators

(ICC ¼ 0.896).2 This method, however, tends to underestimate

corneal thickness when compared with ultrasound pachy-

metry by a mean of 24.82 mm to 38 mm.33 Limits of agreement

were also large, extending from 7.2 to 69 mm.33 Also of

concern is that the standard deviation of differences between

consecutive measurements by IVCM was almost four times

greater than that of consecutive measurements by ultra-

sound pachymetry.

The plane of focus in the RCM is altered by movement of

the objective lens relative to the applanating cap. The focal

plane position (in mm) is automatically displayed on the

computer screen and recorded for each image saved. Corneal

thickness may therefore be obtained from a full thickness

scan of the cornea. Salvetat et al58 has shown that RCM tends

to overestimate corneal thickness comparedwith non-contact

specular microscopy by a mean of 6.5 � 17 mm. The 95% limits

of agreement between instruments ranged between �25.6 mm

and 38.7 mm.58

Examination with the RCM involves corneal contact and

compression, and this inevitably induces artifacts associated

with corneal flattening that may give rise to excessive thick-

ness measurements. Moreover, the accuracy of using this

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x8

technique for corneal pachymetry is limited by ante-

rioreposterior eye movements during the scan. Intra- and

inter-examiner reproducibility has been reported to be

good, although lower than that for non-contact specular

microscopy.58

Currently a number of techniques such as ultrasound, slit-

scanning tomography, and scheimpflug tomography have

been widely accepted as accurate methods to assess corneal

thickness. Thus the current limitations of thickness

measurement inherent to IVCM mean that these other tech-

niques are much more widely used for pachymetry in clinical

practice.

9. Immune/inflammatory cells

Zhivov et al68 were first to report in vivo evaluation of Lang-

erhans cells within the human corneal epithelium. Quantifi-

cation of Langerhans cell density is achieved by counting the

number of these cells per image frame.29,66,68 Keratic precip-

itates have also been quantitatively evaluated in Fuchs het-

erochromic cyclitis. Characteristics such as density, diameter,

area, the ratio between the total size and the body size, and

number of pseudopodia are analyzed.13,27

10. Reading centers

The inter-observer variability of many of the quantitative

parameters discussed in this review raises the question

whether there should be reading centers for IVCM image

analysis. A reading center is a central facility specializing in

the standard evaluation of images. Reading centers are

commonly used in multicenter clinical trials. Usually, each

study center requires certification by the reading center

and must use the center’s imaging protocol.65 In the case of

IVCM, a minimum level of expertise in imaging at each site

and a minimum standard of image quality would therefore

be important factors. Digital images would need to be

transmitted electronically. Therefore, all patient identifying

information must be removed to protect patient confidenti-

ality. Additionally, images must not be compressed as this

would degrade image quality. Transmission via the Internet

requires robust security and fast upload speeds. To our

knowledge, the University Hospitals Eye Institute Reading

Center (Cleveland, OH) is currently the only reading center

that provides standardized analysis of corneal endothelial

cell density and morphology and more recently qualitative

and quantitative assessment of the corneal epithelium and

stroma from IVCM images.

11. Conclusion

Quantitative analysis is an increasingly common feature of

studies using IVCM. It is clear that appropriate image selection

and randomization are crucial prior to analysis. There are

several software applications available for quantifying IVCM

images, each with specific advantages and disadvantages.

There are also multiple ways of defining morphological

parameters and there is currently no consensus regarding

“gold standard” definitions of parameters such as sub-basal

nerve density, making comparisons between different

studies difficult. Standardization of IVCM image analysis

through the development of reading centers will be crucial for

any future large multicenter clinical trials.

12. Method of literature search

Searches were performed using PubMed and Medline, and all

years were searched. The following search terms were used:

In vivo confocal microscop, AND cornea, corneal endothelium,

corneal epithelium, keratocyte, sub-basal nerves, corneal haze,

corneal reflectivity, corneal thickness, repeatability, and

reproducibility.

All articles judged to be of relevance to quantitative anal-

ysis of IVCM images were included, and case reports were

excluded. English articles and non-English language articles

with English abstracts were included and relevant non-

English language papers were translated. Pertinent articles

referenced by retrieved articles were also included.

13. Disclosure

The authors reported no proprietary or commercial interest in

any product mentioned or concept discussed in this article.

r e f e r e n c e s

1. Benitez del Castillo JM, Wasfy MAS, Fernandez C, et al. Anin vivo confocal masked study on corneal epithelium andsubbasal nerves in patients with dry eye. Invest OphthalmolVis Sci. 2004;45:3030e5

2. Brugin E, Ghirlando A, Gambato C, et al. Central cornealthickness: Z-ring corneal confocal microscopy versusultrasound pachymetry. Cornea. 2007;26:303e7

3. Ceresara G, Fogagnolo P, De Cilla S, et al. Corneal involvementin crohn’s disease: an in vivo confocal microscopy study.Cornea. 2011;30:136e42

4. Dabbah MA, Graham J, Petropoulos I, et al. Dual-modelautomatic detection of nerve-fibres in corneal confocalmicroscopy images. Med Image Comput Comput Assist IntervInt. 2010;13(Pt 1):300e7

5. De Cilla S, Ranno S, Carini E, et al. Corneal subbasal nerveschanges in patients with diabetic retinopathy: an in vivoconfocal study. Invest Ophthalmol Vis Sci. 2009;50:5155e8

6. Doughty MJ, Muller A, Zaman ML. Assessment of thereliability of human corneal endothelial cell-densityestimates using a noncontact specular microscope. Cornea.2000;19:148e58

7. Eckard A, Stave J, Guthoff RF. In vivo investigations of thecorneal epithelium with the confocal rostock laser scanningmicroscope (rlsm). Cornea. 2006;25:127e31

8. Efron N, Edwards K, Roper N, et al. Repeatability of measuringcorneal subbasal nerve fiber length in individuals with type 2diabetes. Eye Contact Lens. 2010;36:245e8

9. Erie JC, McLaren JW, Hodge DO, et al. The effect of age on thecorneal subbasal nerve plexus. Cornea. 2005;24:705e9

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x 9

10. Ferreira A, Morgado AM, Silva JS. A method for corneal nervesautomatic segmentation and morphometric analysis.Comput Methods Programs Biomed. 2012;107(1):53e60

11. Grupcheva CN, Wong T, Riley AF, et al. Assessing the sub-basal nerve plexus of the living healthy human cornea byin vivo confocal microscopy. Clin Experiment Ophthalmol.2002;30:187e90

12. Harrison DA, Joos C, Ambrosio R Jr. Morphology of cornealbasal epithelial cells by in vivo slit-scanning confocalmicroscopy. Cornea. 2003;22:246e8

13. Hasler S, Thiel MA, Becht CN. In vivo confocal microscopy ofkeratic precipitates in fuchs heterochromic uveitis syndrome.Klin Monbl Augenheilkd. 2009;226:237e40

14. Hertz P, Bril V, Orszag A, et al. Reproducibility of in vivocorneal confocal microscopy as a novel screening test forearly diabetic sensorimotor polyneuropathy. Diabet Med.2011;28:1253e60

15. Hillenaar T, Cals RHH, Eilers PHC, et al. Normativedatabase for corneal backscatter analysis by in vivoconfocal microscopy. Invest Ophthalmol Vis Sci.2011;52:7274e81

16. Hillenaar T, Sicam VADP, Vermeer KA, et al. Wide-rangecalibration of corneal backscatter analysis by in vivoconfocal microscopy. Invest Ophthalmol Vis Sci.2011;52:2136e46

17. Hillenaar T, Weenen C, Wubbels RJ, et al. Endothelialinvolvement in herpes simplex virus keratitis: an in vivoconfocal microscopy study. Ophthalmology.2009;116:2077e86

18. Holmes TJ, Pellegrini M, Miller C, et al. Automated softwareanalysis of corneal micrographs for peripheral neuropathy.Invest Ophthalmol Vis Sci. 2010;51:4480e91

19. Hurmeric V, Sahin A, Ozge G, et al. The relationship betweencorneal biomechanical properties and confocal microscopyfindings in normal and keratoconic eyes. Cornea.2010;29:641e9

20. Imre L, Nagymihaly A. Reliability and reproducibility ofcorneal endothelial image analysis by in vivo confocalmicroscopy. Graefes Arch Clin Exp Ophthalmol.2001;239:356e60

21. Jonuscheit S, Doughty MJ, Ramaesh K. In vivo confocalmicroscopy of the corneal endothelium: comparison of threemorphometry methods after corneal transplantation. Eye.2011;25:1130e7

22. Kallinikos P, Berhanu M, O’Donnell C, et al. Corneal nervetortuosity in diabetic patients with neuropathy. InvestOphthalmol Vis Sci. 2004;45:418e22

23. Kitzmann AS, Winter EJ, Nau CB, et al. Comparison of cornealendothelial cell images from a noncontact specularmicroscope and a scanning confocal microscope. Cornea.2005;24:980e4

24. Klais CMC, Buhren J, Kohnen T. Comparison of endothelialcell count using confocal and contact specular microscopy.Ophthalmologica. 2003;217:99e103

25. Kojima T, Matsumoto Y, Dogru M, et al. The application ofin vivo laser scanning confocal microscopy as a tool ofconjunctival in vivo cytology in the diagnosis of dry eyeocular surface disease. Mol Vis. 2010;16:2457e64

26. Ku JYF, Niederer RL, Patel DV, et al. Laser scanning in vivoconfocal analysis of keratocyte density in keratoconus.Ophthalmology. 2008;115:845e50

27. Labbe A, Dupas B, Offret H, et al. Evaluation of keraticprecipitates and corneal endothelium in fuchs’heterochromic cyclitis by in vivo confocal microscopy. Br JOphthalmol. 2009;93:673e7

28. Ledbetter EC, Scarlett JM. In vivo confocal microscopy of thenormal equine cornea and limbus. Vet Ophthalmol.2009;12(Suppl 1):57e64

29. Lin H, Li W, Dong N, et al. Changes in corneal epithelial layerinflammatory cells in aqueous tear-deficient dry eye. InvestOphthalmol Vis Sci. 2010;51:122e8

30. McLaren JW, Bourne WM, Patel SV. Automated assessment ofkeratocyte density in stromal images from the confoscan 4confocal microscope. Invest Ophthalmol Vis Sci.2010;51:1918e26

31. McLaren JW, BourneWM, Patel SV. Standardization of cornealhaze measurement in confocal microscopy. InvestOphthalmol Vis Sci. 2010;51:5610e6

32. McLaren JW, Nau CB, Kitzmann AS, et al. Keratocyte density:comparison of two confocal microscopes. Eye Contact Lens.2005;31:28e33

33. McLaren JW, Nau CB, Patel SV, et al. Measuring cornealthickness with the confoscan 4 and z-ring adapter. EyeContact Lens. 2007;33:185e90

34. McLaren JW, Patel SV, Nau CB, et al. Automated assessmentof keratocyte density in clinical confocal microscopy of thecorneal stroma. J Microsc. 2008;229(Pt 1):21e31

35. Midena E, Cortese M, Miotto S, et al. Confocal microscopy ofcorneal sub-basal nerve plexus: a quantitative and qualitativeanalysis in healthy and pathologic eyes. J Refract Surg.2009;25(1 Suppl):S125e30

36. Mimura T, Amano S, Fukuoka S, et al. In vivo confocalmicroscopy of hereditary sensory and autonomicneuropathy. Curr Eye Res. 2008;33:940e5

37. Misra S, Craig JP, McGhee CN, et al. Interocular comparison byin vivo confocal microscopy of the 2-dimensionalarchitecture of the normal human corneal subbasal nerveplexus. Cornea. 2012;31(12):1376e80

38. Mitooka K, Ramirez M, Maguire LJ, et al. Keratocyte density ofcentral human cornea after laser in situ keratomileusis. Am JOphthalmol. 2002;133(3):307e14

39. Mocan MC, Irkec M. Fluorescein enhanced confocalmicroscopy in vivo for the evaluation of corneal epithelium.Clin Experiment Ophthalmol. 2007;35:38e43

40. Moller-Pedersen T, Cavanagh HD, Petroll WM, et al. Stromalwound healing explains refractive instability and hazedevelopment after photorefractive keratectomy: a 1-yearconfocal microscopic study. Ophthalmology.2000;107:1235e45

41. Morishige N, Takahashi N, Chikamoto N, et al.Quantitative evaluation of corneal epithelial oedema byconfocal microscopy. Clin Experiment Ophthalmol.2009;37:249e53

42. Oliveira-Soto L, Efron N. Morphology of corneal nerves usingconfocal microscopy. Cornea. 2001;20:374e84

43. Patel DV. In Vivo Confocal Microscopy Of The Cornea InHealth And Disease. Saarbruecken, Germany, LAP LAMBERTAcademic Publishing GmbH & Co, 2011

44. Patel DV, Ku JY, Johnson R, et al. Laser scanning in vivoconfocal microscopy and quantitative aesthesiometry revealdecreased corneal innervation and sensation in keratoconus.Eye. 2009;23:586e92

45. Patel DV, McGhee CN. Contemporary in vivo confocalmicroscopy of the living human cornea using white light andlaser scanning techniques: a major review. Clin ExperOphthalmol. 2007;35:71e88

46. Patel DV, McGhee CN. In vivo laser scanning confocalmicroscopy confirms that the human corneal sub-basal nerveplexus is a highly dynamic structure. Invest Ophthalmol VisSci. 2008;49:3409e12

47. Patel DV, McGhee CNJ. Mapping of the normal humancorneal sub-basal nerve plexus by in vivo laser scanningconfocal microscopy. Invest Ophthalmol Vis Sci.2005;46:4485e8

48. Patel DV, McGhee CNJ. In vivo confocal microscopy of humancorneal nerves in health, in ocular and systemic disease, and

s u r v e y o f o p h t h a lmo l o g y x x x ( 2 0 1 3 ) xex x10

following corneal surgery: a review. Br J Ophthalmol.2009;93:853e60

49. Patel DV, Phua YS, McGhee CN. Clinical and microstructuralanalysis of patients with hyper-reflective corneal endothelialnuclei imaged by in vivo confocal microscopy. Exp Eye Res.2006;82:682e7

50. Patel SV, McLaren JW, Bachman LA, et al. Comparison of flex-center, center, and corner methods of corneal endothelial cellanalysis. Cornea. 2010;29:1042e7

51. Patel SV, McLaren JW, Camp JJ, et al. Automatedquantification of keratocyte density by using confocalmicroscopy in vivo. Invest Ophthalmol Vis Sci. 1999;40:320e6

52. Patel SV, McLaren JW, Hodge DO, et al. Confocal microscopyin vivo in corneas of long-term contact lens wearers. InvestOphthalmol Vis Sci. 2002;43:995e1003

53. Popper M, Morgado AM, Quadrado MJ, et al. Corneal celldensity measurement in vivo by scanning slit confocalmicroscopy: method and validation. Ophthalmic Res.2004;36:270e6

54. Prydal JI, Franc F, Dilly PN, et al. Keratocyte density and size inconscious humans by digital image analysis of confocalimages. Eye. 1998;12(Pt 3a):337e42

55. Rao K, Leveque C, Pflugfelder SC. Corneal nerve regenerationin neurotrophic keratopathy following autologous plasmatherapy. Br J Ophthalmol. 2010;94:584e91

56. Rieth S, Engel F, Buhner E, et al. Comparison of data from therostock cornea module of the heidelberg retina tomograph,the oculus pentacam, and the endothelial cell microscope.Cornea. 2010;29:314e20

57. Rosenberg ME, Tervo TM, Immonen IJ, et al. Corneal structureand sensitivity in type 1 diabetes mellitus. Invest OphthalmolVis Sci. 2000;41:2915e21

58. Salvetat ML, Zeppieri M, Miani F, et al. Comparison betweenlaser scanning in vivo confocal microscopy and noncontactspecular microscopy in assessing corneal endothelial celldensity and central corneal thickness. Cornea. 2011;30:754e9

59. Scarpa F, Grisan E, Ruggeri A. Automatic recognition ofcorneal nerve structures in images from confocal microscopy.Invest Ophthalmol Vis Sci. 2008;49:4801e7

60. Scarpa F, Zheng X, Ohashi Y, et al. Automatic evaluation ofcorneal nerve tortuosity in images from in-vivo confocalmicroscopy. Invest Ophthalmol Vis Sci. 2011;52(9):6404e8

61. Sheng H, Parker EJ, Bullimore MA. An evaluation of theconfoscan3 for corneal endothelial morphology analysis.Optom Vis Sci. 2007;84:888e95

62. Stachs O, Zhivov A, Kraak R, et al. In vivo three-dimensionalconfocal laser scanning microscopy of the epithelial nervestructure in the human cornea. Graefes Arch Clin ExpOphthalmol. 2007;245:569e75

63. Szaflik JP. Comparison of in vivo confocal microscopy ofhuman cornea by white light scanning slit and laser scanningsystems. Cornea. 2007;26:438e45

64. Twa MD, Giese MJ. Assessment of corneal thickness andkeratocyte density in a rabbit model of laser in situkeratomileusis using scanning laser confocal microscopy. AmJ Ophthalmol. 2011;152:941e53

65. Vargo P, Curtin R. Digital reading center requirements forretinal images from clinics in multi-center trials.J Ophthalmic Photogr. 2010;32:92e5

66. Wang Y, Zhao F, Zhu W, et al. In vivo confocal microscopicevaluation of morphologic changes and dendritic celldistribution in pterygium. Am J Ophthalmol.2010;150:650e5

67. Williams R, Fink BA, King-Smith PE, et al. Central cornealthickness measurements: using an ultrasonic instrument and4 optical instruments. Cornea. 2011;30:1238e43

68. Zhivov A, Stave J, Vollmar B, et al. In vivo confocalmicroscopic evaluation of langerhans cell density anddistribution in the normal human corneal epithelium.Graefes Arch Clin Exp Ophthalmol. 2005;243:1056e61

69. Zhivov A, Winter K, Peschel S, et al. [Quantitative analysis ofcorneal subbasal nerve plexus with in vivo confocal laserscanning microscopy]. Klin Monbl Augenheilkd.2011;228:1067e72