Identifying Synapses Using Deep and Wide Multiscale Recursive Networks
This work addresses the challenge of synapse identification in neuroscience, offering a more automated approach for analyzing brain tissue data, though it appears incremental as it adapts an existing network to a new domain.
The paper tackled the problem of identifying synapses in electron microscopy data from fly brain tissue by proposing a deep and wide multi-scale recursive network, achieving considerable improvements over existing techniques that rely on hand-designed features and reducing manual annotation requirements.
In this work, we propose a learning framework for identifying synapses using a deep and wide multi-scale recursive (DAWMR) network, previously considered in image segmentation applications. We apply this approach on electron microscopy data from invertebrate fly brain tissue. By learning features directly from the data, we are able to achieve considerable improvements over existing techniques that rely on a small set of hand-designed features. We show that this system can reduce the amount of manual annotation required, in both acquisition of training data as well as verification of inferred detections.