基于双编码路径融合和双向ConvLSTM的神经元图像分割

doi:10.16088/j.issn.1001-6600.2022101301

Abstract

Abstract: Target segmentation is one of the essential steps in neuronal image analysis. The accuracy of segmentation directly affects the quality of neuronal image analysis and reconstruction. In the face of fuzzy structure boundary, weak noise or weak fiber signal, the existing segmentation methods still have some problems, such as large error and inaccurate recognition signal. To solve these problems, based on the characteristics of neurons, a deep learning network based on dual coding path fusion and bidirectional ConvLSTM is proposed for neuronal image segmentation. Firstly, the network adopts dual coding paths to extract features in the encoder stage, in which the first encoding path adopts dense connection network based on dilated convolution as the fixed feature extractor, and the second encoder adopts deep residual network as the feature extraction network. Then, the densely connected ASPP network is used as a bridge to connect the encoder and decoder. Finally, bidirectional ConvLSTM is used to combine encoder and decoder in skip connection, and a fusion network is introduced in the decoder stage to fuse the features extracted by the two encoders, so as to enhance the spatial information propagation. The results of multiple comparative experiments show that the proposed network can effectively improve the segmentation accuracy of electron microscope neuron images. Sen and Dice on ISBI-2012 and SNEMI3D datasets reaches 0.952 7, 0.958 9 and 0.941 6, 0.912 7, respectively, and the average accuracy is higher than that of U-net and other models.

Key words: image segmentation, neuron, dual coding path, D-ASPP, bidirectional ConvLSTM

CLC Number: TP391.41

QIAN Youwei, HE Fuyun, WEI Yan, FENG Huiling, HU Cong. Neuron Image Segmentation Based on Dual Coding Path Fusion and Bidirectional ConvLSTM[J].Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(3): 67-79.

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Neuron Image Segmentation Based on Dual Coding Path Fusion and Bidirectional ConvLSTM

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