Background: Tumor microenvironment (TME) is now an important direction for cancer prognosis research. For analyzing the immune component within TME, illustrating the distribution of the immune cells in patient tissue samples by immunohistochemistry (IHC) and image quantification analysis are now been applied in many cancer types. However, most of the TME studies only used the tumor microarray to investigate the composition of immune cells in TME, in which the views of immune component are restricted. In addition, with single biomarker staining to stratify therapy strategy shows limited efficacy in treatment since the complicated interactions of the immune components in TME. Based on these shortages, to increase the efficiency and identify the scarce immune subsets, a more systemic analysis approach for immune subpopulations quantification is now imperative. Methods: In this study, we first established an immune cell multiplex IHC staining panel (Opal Polaris, Akoya Bioscience) in non-small cell lung cancer (NSCLC) to investigate the immune components in all the tumor blocks of each patient. In this panel, we applied markers of infiltrating immune cells (CD8, CD68), regulatory markers (PD-1, PD-L1,CD163), and tumor marker (pan-cytokeratin) to explore the TME landscape in NSCLC. Then, with an in-house built quantitative software: SIMPiE, we could analyze the staining signal intensity of each cell markers generated from in Form software to define each lymphocytic immune subsets by phenotyping and quantifying. In addition, SIMPiE (Spatial Image cytometry Multiplex IHC analysis by phyton and in Form based Elements) software allowed us to generate image cytometry data to further explore the spatial context to map the organization and interactions of immune and tumor cells in the TME, and we can define thresholds for the graph to distribute the plurality of the mark points into a plurality of quadrants. Finally, we performed the survival analysis according to the clinicopathological and immune parameters of the patients, and to examine the prognostic significance of each immune subtypes. Results: We have built up an automation quantitative image analysis software to efficiently quantity the signal density and spatial context of all the immune subpopulations in all the tissue sections of each patient, and we further performed the integrative survival analysis to explore the prognostic significance of infiltrating immune cells and immunoregulatory molecules within the TME in NSCLC. Finally, in our study, we found that M1 macrophage patamars showed the most significantly regulatory effect to the patient’s overall survival rate, and M2 macrophage showed less effect compared with M1 macrophage. Conclusions: By this way, we can generate image cytometry data to further explore the spatial context to map the organization of immune and tumor cells in the TME for prognosis analysis.