Automatic characterization and classification of ganglion cells from the salamander retina.

Abstract

The classification of retinal ganglion cells according to their morphological features is addressed by using a comprehensive set of shape measures and several clustering strategies. The morphological features considered include many common measures (such as dendritic radii and the number of dendritic segments) and three new quantifiable measures: 1) the area of influence of the dendritic tree as calculated in an operator-independent manner by using Minkowski sausages; 2) the complexity of tortuousity along each dendritic segment as represented by the 3D bending energy; and 3) the coverage factor as calculated by using the Bouligand-Minkowski fractal dimension, which is more accurate than the commonly used box-counting algorithm. We evaluated four clustering approaches including the k-means and Ward's hierarchical clustering methods. By using these highly quantifiable methods to group the cells into classes, the present work has extended and reassessed the analysis of 68 ganglion cells from the tiger salamander previously classified by Toris et al. ([1995] J. Comp. Neurol. 352:535-559). Though substantiating the number of classes (5) previously proposed by Toris et al., the results obtained here indicate a number of discrepancies among the members of each class, especially regarding the border between two classes, originally called the medium simple and the medium complex cells. Such an effect has motivated the proposal of new names for the medium simple and medium complex classes, now called small highly complex and medium cells, respectively. Also included in the present article are comprehensive statistics of each class, correlations among all the adopted shape measures, and examples of the cells from each class. The resultant classes that emerged were compared using their electrotonic characteristics and physiological profiles.