The invention discloses a multi-modal cerebral apoplexy lesion segmentation method based on small sample learning, and the method comprises the steps: obtaining an original brain training sample which comprises a multi-modal CT medical image and an annotation image, and the original training sample comprises CT, CBF, CBV, MTT, TMax and ischemic cerebral apoplexy lesion tags; the method comprises the following steps: preprocessing a multi-modal medical image, carrying out image augmentation through modes of image deformation, image scaling, generative adversarial and the like, and expanding a small sample image data set; registering the multi-modal image after data augmentation, taking a reference image as a CT image, and performing pixel-level fusion on the multi-modal image after registration; and the fused multi-modal image data is transmitted to a segmentation network constructed based on Transform to carry out image segmentation. The invention further discloses a system using the method. According to the method, the influence on a medical image data segmentation task caused by insufficient data samples is improved, more focus image information is obtained through multi-modal image fusion, and the accuracy of image segmentation is improved.