TY - JOUR T1 - An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging * JF - bioRxiv DO - 10.1101/180430 SP - 180430 AU - S. Wu AU - Y. Toyoshima AU - M.S. Jang AU - M. Kanamori AU - T. Teramoto AU - Y. Iwasaki AU - T. Ishihara AU - Y. Iino AU - R. Yoshida Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/25/180430.abstract N2 - Shifting from individual neuron analysis to whole-brain neural network analysis opens up new re-search opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worm’s head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions. ER -