Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve

Abstract

Quantitative histomorphometry is the current gold standard for objective measurement of nerve architecture and its components. Many methods still in use rely heavily upon manual techniques that are prohibitively time consuming, predisposing to operator fatigue, sampling error, and overall limited reproducibility. More recently, investigators have attempted to combine the speed of automated morphometry with the accuracy of manual and semi-automated methods. Systematic refinements in binary imaging analysis techniques combined with an algorithmic approach allow for more exhaustive characterization of nerve parameters in the surgically relevant injury paradigms of regeneration following crush, transection, and nerve gap injuries. The binary imaging method introduced here uses multiple bitplanes to achieve reproducible, high throughput quantitative assessment of peripheral nerve. Number of myelinated axons, myelinated fiber diameter, myelin thickness, fiber distributions, myelinated fiber density, and neural debris can be quantitatively evaluated with stratification of raw data by nerve component. Results of this semi-automated method are validated by comparing values against those obtained with manual techniques. The use of this approach results in more rapid, accurate, and complete assessment of myelinated axons than manual techniques.

Figure 1

Image Processing Algorithm. Outline and background colors of components correspond to the bitplane colors as described in the text. For a detailed description, please refer to the following subsections in the semiautomated histomorphometry section of Materials & Methods: A, Thresholding and manual fiber debris elimination; B, Axon definition and manual feature elimination; C and D, Fiber separation and further delineation of nonmyelinated profiles; E, Mathematical morphometry.

Figure 2

A. Original gray scale image. B. Red myelin bitplane. During the initial manual myelin thresholding step, all gray level profiles are colored red. C. Axon bitplane. All features with myelin thresholded are colored green. D. Manual elimination of non-axon features initially colored green by the program. Black arrows demonstrate areas where incorrect axon identification is manually eliminated.

Figure 3

Bitplane images and corresponding schematic of the binary imaging process used to achieve fiber separation. A. Initial myelin separation step. Axons, rather than full fiber circumference, are used to identify distinct nerve fibers and overcome the problem of myelin overlap. A 2-pixel overlay of axon onto the myelin maintains the shape and orientation of the profile. B. Ultimate dilation. Axons are maximally dilated without overlap, thereby modeling the dimensions of the myelin surrounding these axons. C. Separated myelin contours. These dilated axons are then superimposed on the formerly identified myelin profiles and Boolean logic is used to reconstitute original myelin shape and configuration. The original myelinated nerve fibers are accurately represented and effectively separated.

Figure 4

A1. Final delineation of myelin from debris and other background. Myelin profiles are initially identified based on intensity only. As a result, debris, cellular infiltrate, and other background with similar intensity to myelin is also identified. A2. The axons are dilated by adding a 2-pixel rim (dotted lines). This dilated rim, which straddles the axon/myelin interface, is called a bitmarker. The bitmarker is then fused with the axon to create a source bitplane. A3. The source bitplane is then transposed onto the myelin-intensity profiles. If the source bitplane is in contact with a myelin-intensity profile, then myelin is confirmed. If the myelin-intensity profile fails this test, it will be filtered out. Myelin is thus counted only if its axon is in the region of interest. B. Final Image for Analysis after automatic fiber debris elimination. C. Image used to measure myelin features. D. Image use to analyze axon features. E. Image used to measure fiber features by combining bitplanes from image C and D utilizing Boolean logic.

Figure 5

Verification of concordance of manual versus digital measurements using a Bland-Altman plot. The absolute difference in the measurements generated by manual versus digital bitplane analysis are shown for (A) fiber area measurements and (B) fiber width.

Figure 6

Regression Analysis. A. A comparison of fiber area between manual and semiautomated methods. B. A comparison of fiber width between manual and semiautomated methods. In both images, the black line is y=x; the blue line is the line of best fit for the data, for which the equation and correlation coefficient are displayed on the graph.

Figure 7

Analysis of Schmidt-Lantermann incisures (clefts). A. Schmidt-Lantermann incisures, which are often attributed to compression injury, appear as concentric layers of myelin with an interposed layer of myelin lamina. B. For standard histomorphometric analysis, the inner myelin ring is used for measures of myelin thickness, as this maintains the original axon-myelin relationship. C. Threshold adjustment can be used to identify Schmidt-Lanterman clefts for specific study of this histopathological entity. Conventional image analysis systems will fail to identify these clefts, and as result the average myelin thickness is distorted due to overestimation of myelination among fibers with these incisures.