Densely Connected Large Kernel Convolutional Network for Semantic Membrane Segmentation in Microscopy Images

Dongnan Liu, Donghao Zhang, Siqi Liu, Yang Song, Haozhe Jia, Dagan Feng, Yong Xia, Weidong Cai
2018 2018 25th IEEE International Conference on Image Processing (ICIP)  
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
more » ... 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. Index Termsneuronal boundary segmentation, electron microscopy image, deep neural network
doi:10.1109/icip.2018.8451775 dblp:conf/icip/LiuZLSJFXC18 fatcat:y6kl6wcumrf4fapztpgd7ocffe