Detection, Count, and Classification of Visual Ganglia Columns of Drosophila Pupae

Abstract

Many neurobiologists use the fruit fly (Drosophila) as a model to study neuron interaction andneuron organization and then extrapolate this knowledge to the nature of human neurological disorders. Recently, the fluorescence microscopy images of fruit-fly neuronsare commonly used, because of the high contrast. However, the detection of the neurons or cells is compromised by background signals, generating fuzzy boundaries. As a result, it is still common that in manylaboratories, the detection, counting, and analysis of this microscope imagery is still a manual task. An automated detection, counting, and morphological analysis of these images can provide faster data processing and easier access to new information. The main objective of this work is to present a semi-automatic detection-counting system and give the main characteristics of images ofthe visual ganglia columns in Drosophila. We present the semi-automatic detection, count, segmentation and we concluded that it is possible to obtain an accuracy of 75% (with a Kappa statistic of 0.50) in the shape classification. Additionally, we develop python GUICC Analyzer which can be used by neurobiology laboratories whose research interests are focused on this topic.