Fig. 5.

Performance of the 5 segmentation networks. To choose the appropriate number of parallel Atrous channels for the segmentation network, we trained five different networks separately. The number of parallel Atrous channels these networks are 1 to 5, respectively. In order to control variables, the training dataset, initial parameters from the classification network and all the meta-parameters (except the number of parallel Atrous channels) of these five networks are the same. We test the performance of the five segmentation networks with 5000 randomly selected micrographs 512*512 pixels in size from the data shown in Table 1 to form a validation dataset. We used intersection-over-union ($\mathit{IOU}=\frac{\mathit{\text{GroundTruth}}\cap \mathit{\text{Segmentation Result}}}{\mathit{\text{GroundTruth}}\cup \mathit{\text{Segmentation Result}}}$) statistical results to judge the performance