RT Journal Article SR Electronic T1 An ensemble learning approach to auto-annotation for whole-brain C. elegans imaging * JF bioRxiv FD Cold Spring Harbor Laboratory SP 180430 DO 10.1101/180430 A1 S. Wu A1 Y. Toyoshima A1 M.S. Jang A1 M. Kanamori A1 T. Teramoto A1 Y. Iwasaki A1 T. Ishihara A1 Y. Iino A1 R. Yoshida YR 2017 UL http://biorxiv.org/content/early/2017/08/25/180430.abstract AB 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.