Ischemic stroke is a common neurological disease with a high mortality rate. Accurate stroke segmentation is crucial to evaluating patients’ disease outcomes. Stroke segmentation suffers from variable lesion size and noise interference due to similar image contrast characteristics of tissue structures. In this work, we propose a novel two-stage framework named Segmentation–reSegmentation Net (SrSNet) that mainly considers the bilateral symmetric comparison of brain hemispheres to locate pathological abnormalities. The whole framework includes two stages, the coarse segmentation stage and the fine segmentation stage. In the coarse segmentation stage, the brain images are first divided into three patches according to their spatial information to learn the local-level feature and then fed into a 2D Triplet Multi-scale Symmetric Transformer (TMSFormer) with a feature fusion module. This module facilitates the TMSFormer to generate detailed coarse segmentation results. In the fine segmentation stage, we refine the coarse segmentations with a 2D ResUNet. Extensive experiments are conducted using the two ischemic stroke lesion segmentation datasets, and the results are compared with other advanced models. Experiment results on two datasets show that SrSNet achieves state-of-the-art (SOTA) performance. Moreover, on the Ischemic Stroke Lesion Segmentation 2022 (ISLES’22) dataset, the recall score for stroke lesions that the maximum cross-sectional diameter is larger than 5 cm is 83.80%, and the recall score for strokes that the number of lesions of more than five is 79.05%. Overall, our method provides a potentially successful tool for improving the diagnosis of ischemic stroke.