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Segmentation-Enhanced CycleGAN


Algorithmic reconstruction of neurons from volume electron microscopy data traditionally requires training machine learning models on dataset-specific ground truth annotations that are expensive and tedious to acquire. We enhanced the training procedure of an unsupervised image-to-image translation method with additional components derived from an automated neuron segmentation approach. We show that this method, Segmentation-Enhanced CycleGAN, enables near perfect reconstruction accuracy on a benchmark connectomics segmentation dataset despite operating in a “zero-shot” setting in which only volumetric labels from a different volume imaging method were used.