We are pleased to introduce a first-of-its-kind tool that combines in-silico region detection and missing value estimation for spatially resolved transcriptomics. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for each detected region. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify intra-tissue heterogeneity and recover spatial gene-gene co-expression signals. We therefore strongly encourage using MIST before downstream ST analysis because it provides unbiased region annotations and enables accurately denoised spatial gene-expression profiles.