基于医学影像分割方法的多模态语料库构建

Electronic medical records(EMRs) corpus provides qualitative diagnosis results of related medical images. However, the good management of medical data may be affected due to the lacking of labeled images and texts and it is hard for medical students to acquire the related medical knowledge independently. To solve this problem, a medical image segmentation method based on the deep level set algorithm is proposed to segment medical images automatically and output contour results of the interested area and related quantitative indicators. Electronic medical record text is annotated grounded on natural language processing methods. The information representation of medical record texts and images of multimodal corpus is enhanced. Experimental results on the glaucoma image dataset show that the proposed method segments the optic disc and the optic cup in the fundus image accurately and a multimodal corpus with self-evident labeled images and EMRs is constructed effectively as well.