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Quantifying lesions in a reliable manner is fundamental for studying the effects of neuroanatomical changes related to recovery in the post-stroke brain. However, the wide variability in lesion characteristics across individuals makes manual lesion segmentation a challenging and often subjective process. This often makes it difficult to combine stroke lesion data across multiple research sites, due to subjective differences in how lesions may be defined. Thus, we developed the Semi-automated Robust Quantification of Lesions (SRQL; https://github.com/npnl/SRQL; DOI: 10.5281/zenodo.557114) Toolbox that performs several analysis steps: 1) a white matter intensity correction that removes healthy white matter voxels from the lesion mask, thereby making lesions slightly more robust to subjective errors; 2) an automated report of descriptive statistics on lesions for simplified comparison between or across groups, and 3) an option to perform analyses in both native and standard space to facilitate analyses in either space. Here, we describe the methods implemented in the toolbox.