The segmentation model construction and training based on single source data may lead to insufficient segmentation accuracy due to the defects of various imaging methods in medical images. Aiming at this problem, a medical image segmentation method based on multi-source information fusion was proposed. The FFDM and DBT data sources in the breast tumour microcalcification cluster lesion were used as examples to verify the effectiveness of the proposed method. The Yolov4 region candidate network was used to screen the suspicious regions of the FFDM data. DBT image was preprocessed by using the suspicious region information. The preprocessed DBT image was used as the input of the improved U-Net model to achieve lesion segmentation. Finally, through the fusion strategy of fault segmentation results based on sequential similarity discrimination, the multi-slice results in DBT were combined to complete the final lesion segmentation. True positive rate of 98.52%, false positive rate of 10.45% and accuracy of 94.07% were obtained from the FFDM and DBT data of 20 patients by using this method. Results show that the medical image segmentation method based on multi-source information fusion can effectively utilize the advantages of multi-source data, and achieve the rapid and accurate segmentation of lesions. The method can provide a novel solution for intelligent medical image diagnosis and treatment.