Standardization of Size, Shape and Internal Structure of Spinal Cord Images: Comparison of Three Transformation Methods

<div><p>Functional fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular dynamics in the brain and spinal cord. However, it is difficult to integrate spatial information obtained from imaging data in specific regions of interest across multiple samples, due to large variability in the size, shape and internal structure of samples. To solve this problem, we attempted to standardize transversely sectioned spinal cord images focusing on the laminar structure in the gray matter. We employed three standardization methods, the affine transformation (AT), the angle-dependent transformation (ADT) and the combination of these two methods (AT+ADT). The ADT is a novel non-linear transformation method developed in this study to adjust an individual image onto the template image in the polar coordinate system. We next compared the accuracy of these three standardization methods. We evaluated two indices, i.e., the spatial distribution of pixels that are not categorized to any layer and the error ratio by the leave-one-out cross validation method. In this study, we used neuron-specific marker (NeuN)-stained histological images of transversely sectioned cervical spinal cord slices (21 images obtained from 4 rats) to create the standard atlas and also to serve for benchmark tests. We found that the AT+ADT outperformed other two methods, though the accuracy of each method varied depending on the layer. This novel image standardization technique would be applicable to optical recording such as voltage-sensitive dye imaging, and will enable statistical evaluations of neural activation across multiple samples.</p></div

Sample images of spinal cord.

<p>(<b>A</b>) Isolated lower brainstem and cervical spinal cord. (<b>B</b>) Neuron-specific marker NeuN-stained histological image of the cross-section of the spinal cord at C4 level. A NeuN-stained image showed clearly demarcated laminar structures of the gray matter of the spinal cord.</p

Spatial distribution of categorized pixels and ambiguous pixels.

<p>Black colored regions represent ambiguous pixels that were categorized with the probability less than 95% (upper) and 80% (bottom) threshold level, respectively. In this figure, we can overview the spatial precision of each transformation method.</p

Extraction of the outline based on the polar coordinate system.

<p>(<b>A</b>) A schematic drawing of the extraction of the outline. (<b>B</b>) A development of the smoothed outline in - space. <b>a</b> and <b>b</b> indicate the maximum points, and <b>c</b> and <b>d</b> indicate the minimum on the outline function in the - space, and these points were used as ground control points (GCPs) to estimate the parameters for the AT.</p

Conversion of a histological image to binary images of laminar structure.

<p>(<b>A</b>) Histological image data of the cross-section at the 4th cervical spinal cord. The boundaries of laminas in the gray matter were demarcated by solid lines. (<b>B</b>) Binary images corresponding to each lamina and the white matter. The pixels which belong to a lamina were set to 1 and blotted out in white, and the other pixels were blotted out in black.</p

A schematic drawing of the standardization process.

<p>Individual sample image was projected onto the template image through the affine transformation (AT), angle-dependent transformation (ADT) and combination of these two methods (AT+ADT).</p

Comparisons of the mean error ratio estimates for type 1 error (A) and type 2 error (B).

<p>The significance of the differences were evaluated by two-way ANOVA with Bonferroni's post hoc test, and results with significance level <0.05 (Bonferroni-corrected <0.05/3 = 0.017) were marked with “*”.</p

Number and percentage of pixels belonging to each lamina in the transformed image.

<p>The bold type indicate the method which gave the smallest number of pixels for each layer.</p><p>Medial and lateral portions of lamina V were classified into V-m and V-l, respectively.</p

Spatial averaging of histological images.

<p>(<b>A</b>) Averaged histological image without standardization. It was constructed by averaging all histological images to adapt to the position of the central canal. (<b>B</b>) Standardized anatomical image. All histological images were transformed using the AT+ADT method, and the averaged image was constructed from them.</p