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

Abstract

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.

Why Not a Single Image? Combining Visualizations to Facilitate Fieldwork and On-Screen Mapping

“…The area is entirely covered by forest, the exploitation of which ceased when the Biosphere Reserve was declared in 1989. Airborne laser scanning data of an area covering 230 km 2 around Chactún was collected at the end (peak) of the dry season in May 2016. Mission planning, data acquisition, and data processing were done with clear archaeological purposes in mind.…”

“…Some scientific fields have already experienced this requirement for optimization, transparency, and reproducibility of visualization methods. Medical diagnostics, for example, has been facing difficulties of integrating spatial information obtained from imaging data for many years, which finally led to the development of standardizations [1,2]. Hence, it makes sense to strive for unification of visualization methods also in archaeology and cultural heritage management, as well as other disciplines (e.g., geomorphology, geology, natural resource management, glaciology) that use visualized airborne laser scanning data.…”