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Microscopy image fusion, as a new item in related research field, has been extensively used in integrated-circuit defect detection and intaglio-plate-microstructure observation. In this article, a novel microscopy image fusion algorithm based on saliency analysis and adaptive m-pulse-coupled neural network in non-subsampled contourlet transform domain is proposed, in which each original image can be decomposed into a low-frequency subband and a series of high-frequency subbands. A new measurement technique based on image variance permutation entropy is designed for fusion of the low-frequency subbands, and a novel sum-modified Laplacian is chosen as external stimulus which motivates the adaptive m-pulse-coupled neural network for the high-frequency subbands. Yet, the linking strength of the m-pulse-coupled neural network is determined by five features of the saliency map. Then, the selection rules of different subbands are worked based on the corresponding weight measures. Finally, the fusion image is reconstructed via inverse non-subsampled contourlet transform. Experimental results reveal that the proposed algorithm achieves better fused image quality than other traditional representative ones in the aspects of objective evaluation and subjective visual.