Multimodal Corpus Construction Based on Medical Image Segmentation Algorithm
LIN Yuping1, ZHENG Yaoyue2, ZHENG Haojie2, ZHANG Dong3, WANG Cong2, LI Xiaomian1, LI Yingyu1, TIAN Zhiqiang2
1. School of Foreign Studies, Xi′an Jiaotong University, Xi′an 710049 2. School of Software Engineering, Xi′an Jiaotong University, Xi′an 710049 3. School of Automation Science and Engineering, Xi′an Jiaotong University, Xi′an 710049
Abstract：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.
林玉萍, 郑尧月, 郑好洁, 张栋, 王丛, 李小棉, 李颖玉, 田智强. 基于医学影像分割方法的多模态语料库构建[J]. 模式识别与人工智能, 2021, 34(4): 353-360.
LIN Yuping, ZHENG Yaoyue, ZHENG Haojie, ZHANG Dong, WANG Cong, LI Xiaomian, LI Yingyu, TIAN Zhiqiang. Multimodal Corpus Construction Based on Medical Image Segmentation Algorithm. , 2021, 34(4): 353-360.
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2021, Vol. 34 
