RESUMEN
We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.
Asunto(s)
Encéfalo/fisiología , Procesamiento de Imagen Asistido por Computador/métodos , Sinapsis/fisiología , Animales , Encéfalo/citología , Bases de Datos Factuales , Drosophila melanogaster , Microscopía Electrónica , Vías NerviosasRESUMEN
We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.