Development of Brain Atlas for Quantitative Comparison of Anatomical Parcellations

Abstract

Various research efforts have reported reference discrete macro-anatomical regions of the brain which were delineated according to specific brain atlas or parcellation protocol. For now, there is no widely accepted standards for partitioning the cortex and subcortical structures as well as assigning labels to the resulting regions. Previous attempts to reconcile neuroanatomical nomenclatures have been mostly qualitative while concentrating on the development of thesauri or simple semantic mappings between terms. In order to overcome this problem, we have developed a brain atlas method which is suitable for different quantitative analysis of the brain. The interaural 11.28mm and bregma 2.28mm region of rat brain in stereotaxic coordinates has been employed to demonstrate this method. The magnetic resonance imaging (MRI) scan of this brain region was imported into the Surfer (Golden Software, LLC) application. Each brain regions were traced and polygons were superimposed on these regions. The polygons were then encoded with region names as well as location information. The polygons were then exported as shapefile and the shapefile was read into python IDE using the geopandas library (the result is shown in figure 1(A)). For segmentation, each brain regions were labelled with unique numbers while for other brain feature analysis, experimental measurements were prepared as unique columns in microsoft excel data sheets. The data sheets were then merged with shapefile to get the final form of the atlas. Using the numerical data, the atlas was then reconstructed using python codes. The two cases of brain segmentation are shown in figures 1(C) and 1(D) while the case of tissue feature distribution across the rat brain are presented in figure 1(B). The advantage of the method used for this study is the memory management and how fast results can be obtained. Furthermore, this method can easily be extended to the human brain.