The accurate segmentation of brain stroke lesions in medical images are critical for early diagnosis, treatment planning, and monitoring of stroke patients. In recent years, deep learning-based approaches have shown great potential for brain stroke segmentation in both MRI and CT scans. However, it is not clear which modality is superior for this task. This paper provides a comprehensive review of recent advancements in the use of deep learning for stroke lesion segmentation in both MRI and CT scans. We compare the performance of various deep learning-based approaches and highlight the advantages and limitations of each modality. The deep learning models for ischemic segmentation task are evaluated using segmentation metrics including Dice, Jaccard, Sensitivity, and Specificity.