… quent neuron reconstruction process. To our best knowledge, deep-learning based neuron
reconstruction … In [51], deep reinforcement learning is employed for neural tracking in 2D …

… varieties of neuron morphologies among samples, we adopt residual learning and inception
learning to build the deep network containing multi-scale information of neural structures. …

… Using the trained models, we filtered out falsely detected signals and pruned the reconstructed
neuronal tree. Furthermore, different tracing results generated from multiple base tracing …

… Our purpose is to reconstruct neurons in tangled neuronal images via … neuron segmentation
with deep networks. We introduce briefly image segmentation works based on deep learning…

… of deep learning-based methods for various optical neuron … deep learning method for
automatic neuron reconstruction. A 3D deep residual CNN was employed for accurate neuron …

… Deep learning has recently been applied to solving inverse problems in imaging science …
In this work, we used deep learning to rapidly perform phase recovery and reconstruct complex-…

… To ensure the quality of reconstructed neurons and provide guidance for annotators to … ,
we propose a deep learning based quality control method for neuron reconstruction in this paper…

… The present study is to develop a deep learning technique for SPECT image reconstruction
that … training method specifically applicable to medical image reconstruction is presented. …

… introduced a similar convolutional neural network (CNN) for low-dose CT denoising. In the
… deep learning methods were also proposed for MR reconstruction such as variational neural …

… pipeline to perform automatic neuron reconstruction. In this work, we propose a deep
learning approach for improving the accuracy of 3D neuron reconstruction. First, we propose to …