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The automatic reconstruction of single neuron cells from microscopic images is essential to establishing the research on neuron morphology. However, the performance of single neuron reconstruction algorithms is constrained by both the quantity and the quality of the annotated 3D microscopic images since annotating large-scale single neuron models is highly labour intensive. We propose a framework for synthesizing microscopy-realistic 3D neuron images from simulated single neuron skeletons using conditional Generative Adversarial Networks (cGAN). We build the generator network with multi-resolution sub-modules to improve the output fidelity. We evaluate our framework on Janelia-Fly dataset from the BigNeuron project. With both qualitative and quantitative analysis, we show that the proposed framework outperforms the other state-of-the-art methods regarding the quality of the synthetic neuron images. We also show that combining the real neuron images and the synthetic images generated from our framework can improve the performance of neuron segmentation.

Structural analysis of neurons can provide valuable insights of brain function. Semantic segmentation of neurons thus becomes an important technique in bioinformatics. Deep learning approaches have shown promising performance in various semantic segmentation problems. However, segmentation of neurons in Electron Microscopy (EM) images has some differences compared with typical segmentation tasks due to the image noise and the disturbance of the intracellular structures. In our work, we propose a network with a ResNet encoder and densely connected decoder with large kernels, and then refinement with simple morphological post-possessing. Two main advantages of our method are: 1) the network can prevent the loss of high-resolution information and enlarge the reception field; 2) the post-processing method is simple and can be directly applied to the probability map from the network to enhance the unconfident area. Evaluated on the ISBI2012 EM membrane segmentation challenge, the proposed method achieves competitive performance.

2D neuron membrane segmentation for Electron Microscopy (EM) images is a key step in the 3D neuron reconstruction task. Compared with the semantic segmentation tasks for general images, the boundary segmentation in EM images is more challenging. In EM segmentation tasks, we need not only to segment the ambiguous membrane boundaries from bubble-like noise in the images, but also to remove shadow-like intracellular structure. In order to address these problems, we propose a Large Kernel Refine Fusion Net, an encoder-decoder architecture with fusion of features at multiple resolution levels. We incorporate large convolutional blocks to ensure the valid receptive fields for the feature maps are large enough, which can reduce information loss. Our model can also process the background together with the membrane boundary by using residual cascade pooling blocks. In addition, the postprocessing method in our work is simple but effective for a final refinement of the output probability map. Our method was evaluated and achieved competitive performances on two EM membrane segmentation tasks: ISBI2012 EM segmentation challenge and mouse piriform cortex segmentation task.

By using the fixed point theorems and the decomposition and synthesis technique, the existence of mild solutions for impulsive neutral integro-differential equations with advanced argument is established. An example to illustrate our main results is given.