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The segmentation of neuronal morphology in electron microscopy images is crucial for the analysis and understanding of neuronal function. However, most of the existing segmentation methods are not suitable for challenging datasets where the neuronal structure is contaminated by noise or has interrupted parts. In this paper, we propose a segmentation method based on deep learning to determine the location information of neurons and reduce the influence of image noise in the data. Specifically, we adapt our neuron dataset based on UNet by using convolution with BN fusion and multi-input feature fusion. The method is named REDAFNet. The model simplifies the model structure and enhances the generalization ability by fusing the convolution layer and BN layer. The noise interference in the data was reduced by multi-input feature fusion, and the ability to understand and express the data was enhanced. The method takes a neuron image as input and its pixel segmentation map as output. Experimental results show that the segmentation accuracy of the proposed method is 91.96%, 93.86% and 80.25% on the ISBI2012 dataset, U-RISC retinal neuron dataset and N2DH-GOWT1 stem cell dataset, respectively. Compared with the existing segmentation methods, the proposed method can extract more complete feature information and achieve more accurate segmentation. [ABSTRACT FROM AUTHOR]

Accurate segmentation of knee cartilage in MR images is crucial for early diagnosis and treatment of knee conditions. Manual segmentation is time-consuming, leading researchers to explore automatic deep learning methods. However, the choice between 2D and 3D networks for organ segmentation remains debated. In this paper, we propose a hybrid 2D and 3D deep neural network approach, named UVNet, which combines the strengths of both techniques to enhance segmentation performance. Within this network structure, the 3D segmentation network serves as the backbone for feature extraction, while the 2D segmentation network functions as an information supplement network. Local and global MIP images are generated by employing various maximum intensity projection modes of knee MRI volumes as input for the information supplement network. By constructing a local and global MIP feature fusion module, the supplementary information obtained from the 2D segmentation network is fully integrated into the backbone network. We assess the quality of the proposed method using the Osteoarthritis Initiative (OAI) dataset and the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset, comparing it to the Baseline Network and other advanced 2D and 3D segmentation methods. The experiments demonstrate that UVNet achieves competitive performance in the aforementioned two cartilage segmentation tasks. [ABSTRACT FROM AUTHOR]