Information Processing for Medical Image Understanding

这个研究项目可以用一个简单的问题来表述：“计算机科学如何能提高医生对疾病过程的理解？”“在拟议的工作中，复杂信息的新颖显示将为探索代谢过程和指导外科医生切除所有癌性肿瘤提供工具；在骨科中，改进的数学模型将用于解决骨关节炎在医学图像中高度可变的表现。该建议是利用申请人在计算方面的专业知识和在外科方面的丰富经验，提出将直接改善加拿大人健康的新的理论结果和实施。新颖的显示器将包括质谱数据的听觉呈现。在皇后大学，我们有几台仪器可以捕获各种组织的质谱图像，或者可以捕获外科电刀切割组织时的数据流。这些数据是高度多光谱的，很难自动分类。相反，我建议将大量数据转换为可听输出。这将使研究组织样本的病理学家，或在手术室里做实时决策的外科医生，真正听到癌症的声音。如果能够听到样本中每个细胞的代谢产物，将是癌症研究组织分析领域的世界领先突破。数学建模将包括在高维数学流形上执行计算的方法。首先，我们将建立基于流形的关节炎关节统计模型；接下来，我们将使用统计模型来提高对三维图像（如CT或MRI扫描）中关节炎关节的自动识别。关节炎是影响几乎每个加拿大家庭的一系列疾病，因此改进描述它的方法将有助于对这种使人衰弱的疾病进行分期治疗。24名高素质人员将接受多学科培训，为他们在生物医学计算领域的职业生涯做好准备。我们的毕业生受到医学院、研究生院和信息技术行业的高度追捧。

This research program can be phrased as a simple question: ``How can computer science improve a physician's understanding of disease processes?'' In the proposed work, novel displays of complex information will provide tools for exploring metabolic processes and guiding a surgeon to excise all of a cancerous tumor; in orthopedics, improved mathematical modeling will be used to address the highly variable presentation of osteoarthritis in medical images. This proposal is to use the applicant's expertise in computing, and considerable experience in surgery, to give new theoretical results and implementations that will directly improve the health of Canadians.***The novel displays will include the auditory presentation of data from mass spectrometry. At Queen's, we have several instruments that can variously capture mass-spectrometry images of tissue, or can capture a data stream as a surgical electric knife slices through tissue. These data are highly multi-spectral and are very difficult to classify automatically. Instead, I propose to transform the mass data to an audible output. This will let a pathologist studying a tissue sample, or a surgeon making real-time decisions in an operating room, to literally hear the sound of cancer. Being able to hear the metabolic products of each cell in a sample would be a world-leading breakthrough in the analysis of tissues for cancer research.***The mathematical modeling will include ways to perform computations on high-dimension mathematical manifolds. First, we will develop statistical models of arthritic joints based on the manifolds; next, we will use the statistical models to improve the automatic recognition of arthritic joints in 3D images such as CT or MRI scans. Arthritis is a constellation of diseases that affects nearly every Canadian family, so improved ways of describing it will help the staging of treatments for this debilitating condition.***Twenty-four highly qualified personnel will receive multidisciplinary training to prepare them for careers in biomedical computing. Our graduates are highly sought after by medical schools, graduate schools, and information-technology industry sectors.