医学影像处理技术辅助小肠疾病诊断

项目名称： 医学影像处理技术辅助小肠疾病诊断

项目编号： No.61303094

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 武星

作者单位： 上海大学

项目金额： 23万元

中文摘要： 小肠运动功能分析结果是对胃、肠疾病进行诊断的重要依据。提出两类医学影像处理方法分析小肠运动功能：一类是面向微观小肠片段的分割方法，另一类是面向宏观小肠区域的相异度度量方法。第一类方法包括AILBF算法和BLFM算法。AILBF算法的研究包括：轮廓自动初始化与改进局部二值拟合模型，目的是提高精确分割图像Level Set方法的效率。BLFM算法的研究包括：边缘方向自适应Fast Marching方法与移动基准，以解决低对比度图像中边界溢出的问题，并且提高快速分割图像Fast Marching方法的准确率。第二类方法是DNMI算法，其研究负互信息度量相邻磁共振图像小肠区域相异性的问题,突破了传统面向小肠片段运动功能分析的局限。两类方法结合有助于胃、肠疾病，特别是危及生命的胃、肠出血的迅速诊断，对救治病人有重要意义。

中文关键词： 小肠；电影磁共振成像；全卷积网络；长短期记忆单元；相异度

英文摘要： The quantitative assessment of small bowel motility plays a pivotal role in the diagnosis of small intestinal disease. Two types of medical image processing methods are proposed to assess the small bowel motility. One type is the small bowel segments oriented method and the other is the small bowel area oriented. The small bowel segments oriented method includes AILBF algorithm and BLFM algorithm. The research about AILBF includes not only the automatic contour initialization but also an improvement method for the Level Set method with local binary fitting model. AILBF algorithm aims to boost the efficiency of traditional Level Set method, which is known as an accurate contour detection method. The research about AILBF includes orientation adaptive Fast Marching method and moving benchmark line, which aims at addressing the leakage problem owing to the presence of low contrast areas. BLFM algorithm aims to improve the accuracy of Fast Marching method, which is characterized as a fast propagation method. The small bowel area oriented method includes the DNMI algorithm that utilize the negative mutual information to detect the difference of small bowel areas between adjacent MR images, which break through the limit of traditional small bowel segments oriented method. Our research can boost the efficiency of diagn

英文关键词： small bowel；Cine-MRI；fully convolutional networks；long short-term memory；difference