Title: Morphological Error Detection in 3D Segmentations,
Abstract: Deep learning algorithms for connectomics rely upon localized classification,
rather than overall morphology. This leads to a high incidence of erroneously
merged objects. Humans, by contrast, can easily detect such errors by acquiring
intuition for the correct morphology of objects. Biological neurons have
complicated and variable shapes, which are challenging to learn, and merge
errors take a multitude of different forms. We present an algorithm, MergeNet,
that shows 3D ConvNets can, in fact, detect merge errors from high-level
neuronal morphology. MergeNet follows unsupervised training and operates across
datasets. We demonstrate the performance of MergeNet both on a variety of
connectomics data and on a dataset created from merged MNIST images.