2.1.

Preparation and Staining of Mohs Surgical Skin Excisions

Skin excisions from Mohs surgery were obtained from our collaborating Mohs surgeon in the Dermatology Service at Memorial Sloan-Kettering Cancer Center, following approval from our Institutional Review Board. During surgery, each excision is processed by a Mohs technician and 3 to 5 frozen histology sections are prepared, each section being $\sim 5\text{to}6\mathrm{mm}$ thin. After the Mohs surgeon has examined the frozen sections, the remainder of the excision is discarded. These discarded excisions were collected. Often, triangular-shaped excisions of normal skin are made adjacent to the crater-shaped wound that remains after removal of the tumor. Such excisions are necessary for cosmetically efficient suturing and closing of the wound. These excisions are called “dog-ears” because of their shape. Fresh normal dog-ear skin excisions were also collected soon after they were discarded.

The collected Mohs excisions were frozen in the embedding medium that is used to prepare histology sections. Each excision was thawed, rinsed in isotonic saline solution for $30\mathrm{s}$ , and placed in a solution of acridine orange (Fluka, 01660) in saline for various times and concentrations. Concentrations of $0.3\mathrm{mM}$ and $1.0\mathrm{mM}$ were tested for staining skin excisions by immersing for $5\mathrm{s}$ , $20\mathrm{s}$ , $1\min$ , $3\min$ , and $5\min$ . Five excisions were tested for each concentration and time, resulting in a total of 50. After staining, excess dye was rinsed from the dermis by dipping in isotonic saline solution for $2\text{to}3\mathrm{s}$ . The excisions were mounted in a custom-designed tissue fixture and imaged with a confocal microscope.

Fifty Mohs surgical excisions of BCCs were imaged in the process of optimizing the staining procedure. The images were visually evaluated, and the minimum time was determined for brightening of nuclei and detectability of BCCs. The evaluation was limited to only the exposed surface of the excisions since the Mohs surgeon mainly needs to determine the lateral spread of tumor on the excised surface. Concentration of $1\mathrm{mM}$ and immersion time of $20\mathrm{s}$ was chosen as the optimum for rapid staining and strong fluorescence. After this preliminary feasibility test, for further instrumentation development, excisions were stained with $1\mathrm{mM}$ acridine orange for immersion time of $20\mathrm{s}$ . Subsequently, 50 more excisions were similarly stained and imaged for the clinical comparison-to-histology work.

2.2.

Diffusion of Contrast Agent into Tissue

Only the excised tissue surface is examined in Mohs histology, to determine the lateral extent of tumor. Thus, for imaging, staining with acridine orange was necessary only superficially through a few cell layers. A cell layer in skin is about $\sim 10\mathrm{mm}$ thin, and the Mohs surgeon usually examines 3 to 5 frozen histology sections, each being $\sim 5\text{to}6\text{-}\mathrm{mm}$ thin. Hence, we consider imaging to a maximum depth of $\sim 30\mathrm{mm}$ , which corresponds to $\sim 3$ cell layers. (This depth is also approximately the maximum to which real-time confocal imaging is possible in dermal tissue with very low milliwatt power blue $488\text{-}\mathrm{nm}$ illumination.)

Rapid staining depends on the diffusion of the fluorescent dye into the excised tissue. The uptake kinetics of any particular dye depends on molecular weight and tissue conditions such as pressure in interstitial spaces.¹¹ The average time $t$ (s) for diffusion across a distance $x$ (cm) may be determined from the diffusion coefficient $D$ $({\mathrm{cm}}^2∕\mathrm{s})$ as

The diffusion coefficient varies with molecular weight $M_r$ in tumor and normal tissue¹² by the following power law:

2.3.

Confocal Microscope and Tissue Fixture

Figure 1 shows the experimental setup. The confocal microscope was described in detail in our earlier reports on reflectance mosaicing.^{1, 3, 4} Briefly, the original reflectance microscope (VivaScope 2000, Lucid, Inc., Rochester, New York) was modified by incorporating an argon-ion laser for fluorescence excitation at $488\mathrm{nm}$ and a corresponding fluorescence detection channel. The illumination of tissue, is with low-level power of $0.3\text{to}1.0\left(\mathrm{mW}\right)$ . The fluorescence detection optics consists of a dichroic beamsplitter (Chroma, 510DCSPRX), an excitation rejection filter (Omega Optical, 510EFLP) to block extraneous reflected light, and an avalanche photodiode (Perkin-Elmer, Quebec, Canada, C30659-900-R8A). Detection in the fluorescence channel was mainly of the emission from the acridine orange–stained nuclei, with almost none from the cytoplasm. Autofluorescence from the dermis is a few orders of magnitude weaker than the fluorescence from acridine orange and hence was not detected for the low illumination power and tightly confocal (i.e., small pinhole) detection conditions.

Fig. 1

Optical design of the confocal microscope with the following components: argon-ion laser, $7\times$ beam expander (B/E), $488\text{-}\mathrm{nm}$ -excitation selection filter $\left({\mathrm{F}}_1\right)$ , spinning polygon (P), galvanometric scan mirror (G), relay telescopes (T), and objective lens (Obj). A dichroic beamsplitter $(\mathrm{B}∕{\mathrm{S}}_1)$ steers fluorescent emission toward an avalanche photodiode $\left({\mathrm{APD}}_{\mathrm{F}}\right)$ through the $488\text{-}\mathrm{nm}$ -rejection filter $\left({\mathrm{F}}_2\right)$ . A polarizing beamsplitter $(\mathrm{B}∕{\mathrm{S}}_2)$ steers reflected light toward another avalanche photodiode $\left({\mathrm{APD}}_{\mathrm{R}}\right)$ .

A custom-designed water immersion objective lens (StableView, Lucid, Inc.) was used for imaging through a $1\text{-}\mathrm{mm}$ -thick glass slide. Instead of thin coverslips that are conventionally used with objective lenses, a thick glass slide is necessary for mounting and stabilizing unconventional tissue specimens such as surgical excisions. The objective lens features $30\times$ magnification to provide a $430\text{-}\mu \mathrm{m}$ field of view. With a numerical aperture (NA) of 0.9, the calculated axial section thickness is: $\Delta z=1.4\mathrm{n}\lambda ∕{\mathrm{NA}}^2=1.1\mu \mathrm{m}$ and the lateral resolution is: $\Delta x=0.46\lambda ∕\mathrm{NA}=0.25\mu \mathrm{m}$ , which is adequate for imaging nuclear morphology. As previously explained,⁴ water was often substituted with a water-based gel as an immersion medium.

Surgical excisions are often thick, large, and of unusual shape. Furthermore, the tissue is fresh, living, hydrated, and mechanically compliant and hence not easy to mount in a microscope. A custom tissue fixture was engineered for Mohs surgical excisions to be mounted and gently compressed onto a microscope slide. Design details of the original tissue fixture are in an earlier report.⁴ The operation requires a threaded piston to be tightened for gently compressing and embedding in a gel disk, so as to stabilize the excision. However, the rotational motion of the piston caused the gel and subsequently the edges of the tissue to twist and distort. Such distortion is now prevented with a design modification that includes placing a thin polycarbonate disk and needle-roller bearings (Part No. 5909K31, McMaster-Carr, Dayton, New Jersey) between the piston and the gel disk. The fixture allows repeatable and accurate control of the flattening, tip, tilt, sag, and stability of the tissue surface to be imaged. The functionality of the tissue fixture mimics the operation of a cryostat, which is the standard equipment for preparing frozen histology sections for Mohs surgery. Imaging in reflectance was used to guide $z$ distance and tip and tilt adjustments such that the tissue surface was oriented exactly parallel to the focal plane of the objective lens. The process involved translating the mounted excision laterally and adjusting four thumbscrews until the focus moved along the reflective water/tissue interface. This alignment enabled acquisition of images contiguously over large areas of $10\text{to}20\mathrm{mm}$ . (Engineering drawings, manufacturing methods, and operation details are available to researchers who may be interested.)

After the excision is mounted in the tissue fixture and properly positioned and oriented, confocal images were acquired. Images were acquired of the surface of the excision. Because of the thawing, staining, and rinsing process, small distortions in the imaged surface were expected due to the compliance of the tissue. Thus, the mosaics were expected to show a close but not an exactly one-to-one correspondence to the frozen sections that were prepared by the Mohs technician during surgery.

2.4.

Acquisition of Images

The small field of view of the $30\times$ objective lens $\left(430\mu \mathrm{m}\right)$ and the large region of interest ( $\sim 10\text{to}20\mathrm{mm}$ of excised tissue) necessitated the construction of mosaics. A mosaic to display a large area is created by stitching together a two-dimensional (2-D) matrix of confocal images. Mosaics allow observation of large fields of view without sacrificing resolution. The matrix of images was acquired with a continuous step-and-capture routine while translating the tissue fixture with stepper motors–driven linear XY stages (Hayden, Inc., Stamford, Connecticut). An overlap of 10% was included in the translation step distance to correct for a field curvature–induced artifact in the image, as is further explained later. The overlap also prevents loss of detail at the edges between images. The amount of overlap determines the number of images in the matrix that must be acquired, which then determines the total time of acquisition.

Of the 3 to 5 frozen histology sections that are prepared during surgery, the Mohs surgeon usually examines the first to determine the lateral spread of the tumors on the excised surface. Occasionally, if the quality of the first section is poor or if the determination of tumor margins is not very clear, the Mohs surgeon will examine the remaining sections. Additional sections are sometimes prepared if the Mohs surgeon needs to further examine deeper layers of tissue. For this study, however, images were acquired and mosaics created only of the exposed surface of the discarded excisions. This exposed surface corresponds to the last Mohs frozen section. (Subsequent comparison of the mosaics to histology was therefore limited to the last frozen section.)

Confocal mosaics can be quickly acquired. For acquisition, a continuous step-and-capture routine requires about $5\min$ for $36\times 36$ images. Transferring and archiving images followed by processing to create a mosaic requires another $4\min$ on another PC. Thus, total time to create a mosaic is up to $9\min$ at present.

2.5.

Calibrations for Illumination Artifacts and Vignetting

At the edges of images, dark bands due to field curvature–induced artifact and illumination falloff due to vignetting were noticeably seen. These were corrected for in the image-stitching algorithm, based on calibration measurements in the confocal microscope.

The Petzval field curvature in the microscope was calculated to be $\sim 3.8\mu \mathrm{m}$ and measured to be $\sim 5\mu \mathrm{m}$ . When focused at the surface of the excision, field curvature results in tissue being seen in the center but surrounded with an annular ring at the periphery in the image. The ring is due to the overlying glass window and results in an illumination artifact. The artifact appears as bright bands in reflectance but dark bands in fluorescence. By focusing slightly deeper than $5\mu \mathrm{m}$ beneath the tissue surface, the artifact was often minimized. As a result of deeper focusing, small mismatches to the frozen section of the surface and small losses in correlation to frozen histology were anticipated. The dark bands were largely eliminated by cropping the 10% overlap between images in the image-stitching algorithm.

To characterize vignetting, the illumination falloff across the field of view was measured with a standard fluorescent target. A drop of acridine orange was compressed between a microscope slide and coverslip, and an axial stack of images was acquired. The images were averaged in depth to determine the vignetting in both $x$ and $y$ directions (Fig. 2 ). The vignetting was corrected for with an inverted-brightness polynomial fit in the image-stitching algorithm.

Fig. 2

Calibration measurements showing: (a) illumination falloff due to vignetting in the $x$ and $y$ directions across the field of view, and (b) $3\times 3$ mosaic of a reflective grating target indicating angular alignment and lateral registration to within five pixels (which scales down to subpixel in the final mosaics).

2.6.

Calibrations for Angular Misalignments and Lateral Registration

The translation of the linear XY stages must be parallel to the $x$ and $y$ directions of the optical raster scan in the confocal microscope. Mosaics of a reflective grating test target were used to calibrate for angular misalignments. Figure 2 shows a mosaic of a reflective grating target (Ronchi ruling with $200\mathrm{lpi}$ , Edmund Industrial Optics) that demonstrates angular alignment and lateral registration in both $x$ and $y$ directions. The mosaic was created by cropping the 10% overlap between images and stitching $3\times 3$ images. The grating lines appear continuous to within $5\text{pixels}$ between images. However, as explained here, full-size mosaics are scaled down by 8 to $10\times$ such that the lateral mismatch is within a subpixel and not noticeable in the final display.

2.7.

Image-Stitching Algorithm

Mosaics were created with MATLAB software (version 7.4, MathWorks, Natick, Massachusetts). The algorithm implements the following steps: cropping of the overlap between images, merging of images into a single composite mosaic, and correction for the residual dark bands between images. The amount of cropping was 10%, as predefined by the stepping distance of the XY-translation stages during the image acquisition, and further precisely adjusted by measurements of overlap using image analysis software (IPLab Spectrum, version 3.6.5, BD Biosciences, Inc., Rockville, Maryland). Based on our experience, the overlap between images remained repeatedly consistent across large mosaics with minimal errors. After cropping, the images were concatenated into a single composite mosaic. The cropping removes the dark bands due to field curvature. The illumination falloff due to vignetting was then corrected for with inverted-brightness correction polynomial fits in both $x$ and $y$ directions. The polynomials were empirically designed to flatten the illumination falloff across images. The design of the polynomials is specific to our fluorescence mosaics but is based on a destripe filter that was originally authored by Marc Lehman and is available as open-source software called GNU Image Manipulation Program (GIMP). Lehman’s executable and source code can be downloaded at www.GIMP.org.

The pixel gray scale or brightness is defined as $I(x,y)$ , where $x$ and $y$ are column and row positions, respectively, of individual pixels. Horizontally across the entire mosaic, the mean brightness profile $\overline{I}\left(x\right)$ is determined by averaging pixel values in columns as a function of $x$ -pixel location. Similarly, vertically across the entire mosaic, the mean brightness profile $\overline{I}\left(y\right)$ is determined by averaging pixel values in each row as a function of $y$ -pixel location. These are:

Averaging across columns and rows of the entire mosaics provides a globally smoothed low-frequency estimate of the spatial high-frequency variations in fluorescence from the tissue. The mean brightness profiles in Eqs. 3, 4 represent both the fluorescence signal from the central regions of the images and the illumination falloff at the edges. Polynomial fits for brightness in the $x$ direction and the $y$ direction are further modeled in terms of a rolling average of the mean brightness profiles:

In Eqs. 5, 6, the rolling average–based polynomial fits are locally smoothed versions of the mean brightness profiles. The rolling average polynomial fits, too, represent both the fluorescence signals from the central regions and the illumination falloff near the edges of the images. As will be evident from further equations later, the purpose of these polynomial fits is to spatially isolate the regions of low-frequency illumination falloff near the edges of the images from the relatively high-frequency fluorescence signals in the central regions. Isolating the two regions subsequently allows corrections to be applied mainly to the illumination falloff, but none to the fluorescence signals. The parameter $W$ is the window width of a rolling average filter. The width of the filter $W$ was empirically tested in the range of $24\text{to}100\text{pixels}$ . On the basis of visual examination, a small width of $36\text{pixels}$ was found to provide the optimum match of the rolling average polynomial fit to the mean brightness profile. Smaller windows produced too little smoothing of the differences between the spatially high-frequency fluorescence signals in the central regions of the images and the relatively low-frequency illumination falloff near the edges, and therefore provided inadequate isolation of the two regions. This resulted in (unwanted but) minimal corrections to the fluorescence signal but too little correction for illumination falloff. Larger windows resulted in too much smoothing and, again, inadequate isolation, which resulted in too much correction of illumination falloff and also unwanted large corrections to the fluorescence signal.

Subtracting the mean brightness profiles from rolling average polynomial fits substantially removes the fluorescence signals from the central regions and mainly represents the illumination falloff near the edges. This subtraction results in an inverted-brightness correction polynomial that is determined to be:

The inverted-brightness correction polynomials are added back to the original mosaic to flatten the illumination falloff across the images. The illumination falloff at the edges between images are corrected in the $y$ direction (row by row) and in the $x$ direction (column by column) to produce a new brightness or pixel grayscale distribution defined by:

In Eqs. 9, 10, $W$ is the width of rolling average filter (as before), and $D$ is a scaling factor or “gain” for image brightness. Since the corrections are based on a rolling average approximation of the actual pixel brightness distribution, a scaling factor is necessary to adjust the brightness in the regions of fluorescence signal and vignetting to appropriate levels. The scaling factor $D$ was empirically tested in the range of 8 to 128. On the basis of visual examination, a factor of 32 was determined to provide the optimum brightness for the mosaics. Smaller scaling factors result in dark-appearing mosaics, whereas larger scaling factors result in too much brightness and saturation. To every row (or column) of pixel gray scales $I(x,y)$ , the inverted-brightness polynomial fits are applied proportionally to the locally averaged fluorescence signal, as described in Eqs. 9, 10. This approach minimizes the unnecessary corrections in relatively uniformly illuminated central regions of images and limits corrections mainly to the illumination falloff near the edges.

Based on our experience, this algorithm works effectively to correct the well-defined loss of illumination at the edges of images due to vignetting. The algorithm was applied to the 50 mosaics that were acquired to achieve repeatable results. The advantage of this algorithm is that the correction polynomials may be determined in a “blinded” manner to any given mosaic without requiring a priori knowledge of vignetting in the microscope. The mean brightness profiles and rolling average polynomial fits [Eqs. 3, 4, 5, 6] produce an estimate of the illumination falloff due to vignetting. Any additional instrumental errors in illumination are also estimated and corrected for. (An alternative is to directly fit polynomials based on instrument calibrations such as shown in Fig. 2. This may require frequent alignment and measurements of the microscope—a task that is easily performed in laboratory settings but not always in a clinical setting.) The main requirement is that values for $W$ and $D$ must be initially determined in two to three mosaics. We have found that the values may be consistently used afterwards. The disadvantage of this algorithm, however, is unwarranted decrease or increase of contrast in tissue morphologic features, especially at the edges of BCC tumors, due to the unnecessary corrections produced by the smoothing effect of the rolling average model. However, the corrections are close to zero in the fluorescence signal such that visual gain or loss of contrast appeared to be minimal and did not appear to affect subsequent analysis of mosaics and comparison to histology. The final processed mosaic is saved in TIFF format. (The algorithm and MATLAB software, including implementation details, are available to researchers who may be interested.)

2.8.

Display of Large-Area Mosaics

Each image consists of $640\times 480\text{pixels}$ , is $8\text{-}\text{bit}$ gray scale, and requires about $1∕4\mathrm{MB}$ of memory. Thus, a full mosaic of up to $36\times 36$ images consists of $\mathrm{23,040}\times \mathrm{17,280}\text{pixels}$ and requires $325\mathrm{MB}$ of memory. The mosaics are scaled down using bilinear interpolation to make the displayed lateral resolution and pixelation equivalent to that of a $2\times$ -magnified view of histology. The scaling down of the optical resolution and pixelation to match that of a $2\times$ view was explained in detail in our earlier report on reflectance mosaicing.⁴ The final mosaic is displayed to the Mohs surgeon with lateral resolution of $\sim 4\mathrm{mm}$ , consists of $\sim 2500\times 2500\text{pixels}$ , and requires less than $4\mathrm{MB}$ .

Mohs excisions are sometimes oval or elongated in shape and may be as long as $30\mathrm{mm}$ . For such excisions, larger mosaics displaying up to $30\times 10\mathrm{mm}$ were created by acquiring multiple adjacent $10\times 10\mathrm{mm}$ mosaics and joining them in Adobe Photoshop.

We observe mosaics on a $30\text{-}\mathrm{in.}$ flat-screen monitor (Dell 222-7175 with a GeForce 8800 GTS video card) of $2500\times 1600\text{pixels}$ . A full-size mosaic is equivalent to a $2\times$ -magnified view. With digital zooming, smaller portions, called submosaics, are also observed. The display of submosaics is equivalent to higher magnifications of $4\times$ and $10\times$ .

2.9.

Comparison of Mosaics to Frozen Histology

Fifty mosaics were compared to the corresponding Mohs frozen histology sections. The frozen sections were those that were prepared during surgery for the Mohs surgeon. These sections were prepared with standard hematoxylin-and-eosin (H&E) stains. The imaged surface of the frozen-thawed discarded excision corresponds to the final section that was prepared. Therefore, mosaics were compared to the last Mohs frozen section.

Nuclear and morphological features were evaluated in the mosaics and compared to the histology. The Mohs surgeon evaluated those features that are routinely examined in histology and are necessary to detect BCC tumors versus normal skin. The features for the presence of BCC tumors are nuclear pleomorphism (atypical shapes and sizes), increased overall nuclear density, palisading (“picket-fence” type arrangement of nuclei around the inner periphery of tumor), clefting (dark-appearing “moats” around the outer periphery of tumors that are filled with optically clear mucin), and the presence of inflammatory infiltrates. The features for normal skin are epidermal margin (epidermis along half the periphery of the excision), hair follicles, sebaceous glands, and eccrine glands.

The Mohs surgeon uses an objective lens with $2\times$ magnification to quickly examine large areas of histology. Objective lenses with $4\times$ and $10\times$ magnifications are used, when necessary, for closer inspection of nuclear detail. Entire mosaics and submosaics were evaluated at equivalent magnifications. The submosaics usually consisted of a quarter of the entire mosaic, since the Mohs surgeon usually records the presence or absence of BCC tumor in quadrants.

2.J.

Effects of Acridine Orange Staining on Subsequent Frozen Histology

Experiments were performed to investigate the effects of exposure for $20\mathrm{s}$ to $1\mathrm{mM}$ acridine orange on subsequent histology. Possible effects include tissue decay, degradation of RNA and DNA due to autolysis, room-temperature digestion of tissue due to proteolytic enzymes, swelling of cytoplasm, and separation of the epidermis from the dermis. These effects may lead to prevention of H&E staining and a “washed out” appearance. Ten excisions were reprocessed for frozen H&E–stained histology sections after acridine orange staining and confocal imaging. The frozen sections were compared to the corresponding Mohs sections. The evaluation and comparison, performed under “blinded” conditions by the Mohs surgeon, analyzed possible tissue disruptions and distortions as well as the chromaticity of staining.

3.1.

Mosaicing Enables Rapid Examination of Surgical Excisions

Figures 3, 4, 5 show mosaics with significantly improved contrast, compared to those from our earlier studies. The 50 mosaics that were created, for comparison to histology, demonstrate repeatability of the tissue fixturing and mosaicing algorithm. Lateral registration between the edges of images was observed to be subpixel. The mosaics appear reasonably seamless and contiguous with high resolution and reasonably uniform illumination over large areas of tissue and are useful for clinical visualization and comparison to histology. The image quality of the mosaics is repeatable and consistently high for examination of morphologic features that are clinically important for surgical pathology. In Mohs skin excisions, the features include the edges of the tissue, epidermal margins, normal dermis, and nuclear detail and gross morphology of BCCs.

Fig. 3

Confocal mosaic (a) of a micronodular BCC that compares well to the corresponding histology (b). Small and tiny nodules or nests of tumor are seen (T) appearing bright in the mosaic and purple-stained in the histology. (Color online only.)

Fig. 4

Confocal submosaic (a) showing a magnified $6\times$ view of the mosaic in Fig. 3 and comparable histology (b). A micronodular BCC tumor (T) is seen with features such as nuclear pleomorphism, increased nuclear density, palisading (p), and clefting (cl). Normal features are seen such as the epidermis (E), a hair follicle (H), sebaceous glands (S), dermal collagen (C), and inflammatory cells (I).

Fig. 5

Confocal mosaic (a) of an infiltrative BCC. Thin strands of tumor lie in the deeper dermis. The tumor is not clearly visualized in the $2\times$ view but is more obvious in the slightly magnified $4\times$ view (b). The overall disruption of the tissue is evident in the lower-left portion.

3.2.

Confocal Mosaics Compare Well to Frozen Histology for All Types of BCCs: Demonstrates Detectability of Both Large and Small Tumors

Excellent comparison between confocal mosaics and the corresponding Mohs frozen histology was achieved for BCCs as well as normal skin morphology. The epidermal margin of excisions along with the dermo-epidermal junction is clearly and repeatably identified on the mosaics. Normal structures in the dermis such as sebaceous glands, hair follicles, and eccrine glands are easily and consistently visualized. The gross morphology of BCCs in terms of shape, size, and location of tumor nests as seen in the mosaics corresponds well to that seen in frozen histology. Additionally, the atypical morphology of nuclei in BCCs in terms of pleomorphism (varying shapes, sizes, orientations, and irregular distributions), crowding (increased density), and palisading is clearly visualized in the mosaics and corresponds well to the histology. Inflammatory infiltrates are seen around BCC nests. Nuclear staining with acridine orange clearly provided enhanced fluorescence contrast of tumors over the background dermis for both large and small types of BCCs. The large types include superficial and nodular, while the small types include micronodular, infiltrative, and sclerosing. Since the large types are easily detectable (including with reflectance contrast), we present the more challenging cases of small types in Figs. 3, 4, 5.

Figure 3 is a $2\times$ -view mosaic of a micronodular BCC and the corresponding histology. The overall tissue is disrupted by small tumors. Such tissue disruption is a gross feature that is usually intuitively identified by the Mohs surgeon. Small nests or nodules of tumor containing bright nuclei are seen in the mosaic, similar to those seen in the histology in terms of location and extent. The “tumor burden,” meaning extent of area occupied by tumor relative to the total appears approximately the same in both. Inflammatory cells around the tumors are also seen. The peripheral edges of the excision are completely delineated all along the excision. Along the top-and-left half portion of the periphery, we see the epidermis with bright but normal nuclei and also the dermo-epidermal junction. (Accurate and consistent detection of epidermis along half the tissue periphery is a must for Mohs pathology.) Other normal features include hair follicles and sebaceous glands, also with normal nuclei. The features are labeled and better appreciated in Fig. 4.

The mosaic shows the ability of acridine orange to provide strong contrast. Close examination, however, of the top-left quadrant of the mosaic reveals residual artifacts of dark bands due to field curvature. The presence of bands reveals the incomplete correction that may happen when the mosaics are acquired at a too-shallow depth below the glass window to tissue surface (for reasons that were explained earlier). The nuclear and morphologic detail and overall mosaic quality appears far superior on our large monitor than that that may be appreciated in small-size printed displays on paper. To display the detail that we routinely observe, a small portion of the mosaic is shown in Fig. 4.

Figure 4 is a submosaic with magnification of $6\times$ that displays a small region of the mosaic in Fig. 3, along with the corresponding histology. The high-magnification view in Fig. 4 illustrates morphology in detail compared to the low-magnification view in Fig. 3, which illustrates overall correlation to histology. Details of observed micronodular BCC morphology are described in the figure caption. The tumors often appear somewhat brighter than normal features due to the increased nuclear density and increased nuclear-to-cytoplasm ratio.

Figure 5 shows a $2\times$ -view mosaic and a $4\times$ -view submosaic that display the smaller infiltrative-type BCC tumors in the deeper dermis. Infiltrative BCCs were seen in 20% of the excisions. Thin strands of tumor were detectable in every case in $2\times$ views or in some cases with slightly higher magnification of $4\times$ . (Infiltrative BCCs had proven undetectable in previous reflectance contrast because they had shape and brightness similar to surrounding collagen fibers and bundles.) Figure 5 is an example of the most challenging cases. Various normal nucleated features in the dermis such as hair follicles, sebaceous glands, glandular cells, and inflammatory cells uptake acridine orange, which then results in substantial background brightness. Therefore, infiltrative tumor strands that are one cell thin are more difficult to detect.

Figure 6 is a $10\times$ -view submosaic that displays infiltrative-type BCC tumor at higher magnification and the corresponding histology. The contiguity of the thin tumor strands allows visual differentiation from the surrounding discrete distribution of inflammatory cells. The challenge for detecting such thin tumor strands within inflammatory cells in confocal mosaics is similar to that in histology.

Fig. 6

Confocal submosaic (a) showing a higher magnification $10\times$ view of the mosaic in Fig. 5 and comparable histology (b). Both images show infiltrative BCC tumor (T) surrounded by inflammatory infiltrates (I). The contiguity of the thin tumor strands differentiates them from the surrounding discrete inflammatory cells. The challenge in detecting such thin tumor strands within inflammatory cells in mosaics is similar to that in histology.

The results clearly show that magnification is important for tumor detection. As an extreme example, detection of the smallest and thinnest tumors is always possible in individual images at $30\times$ magnification. However, for confocal mosaics, $2\times$ magnification supplemented with, whenever necessary, slightly higher $4\times$ magnification proved to be adequate. In Fig. 3, the micronodular tumors are surrounded by relatively sparse inflammatory cells and hence detectable in the $2\times$ view. By comparison, in Fig. 5, the infiltrative BCC tumors are surrounded by dense inflammatory cells and hence required a $4\times$ view to detect. At $4\times$ magnification, normal structures could be easily differentiated from micronodules and thin strands of tumor. Thus, with $2\times \text{to}4\times$ magnifications, mosaics were confidently evaluated for both presence and absence of tumors. When the display magnification increases from $2\times \text{to}4\times$ , the resolution improves from $\sim 4\mu \mathrm{m}\text{to}\sim 2\mu \mathrm{m}$ .

3.3.

Acridine Orange Staining Does Not Affect Subsequent Frozen Histology

The effect of acridine orange staining and confocal mosaicing on subsequent tissue processing and H&E–stained frozen histology was minimal. There was no difference in the staining of the nuclear, cellular, and dermal morphology between the frozen sections that were prepared before and after imaging. The acridine orange–staining process does not alter the tissue in any way and does not adversely affect the ability of subsequent H&E–stained frozen sections to deliver accurate clinical diagnoses. However, there was minor tissue degradation in two (out of ten) excisions. The frozen sections that were prepared after imaging indicated disrupted tissue and inefficient H&E–staining compared to the ones before. Over the course of ten excisions that were reprocessed, we realized that the prolonged exposure of the tissue to our relatively warm laboratory room temperature was causing tissue degradation. Care was then taken to keep the excisions cool in isotonic saline solution before and after imaging and to expedite the reprocessing. This subsequently led to reproducible frozen sections for the remaining eight excisions.