Probabilistic inference in computer vision and medical imaging

计算机视觉和医学成像中的概率推理

• 批准号: 238845-2010
• 负责人: Arbel, Tal
• 金额: $ 3.13万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547113
• 关键词: Probabilistic; inference; computer; vision; medical

Advances in medical imaging have permitted a large amount of information to be made available to medical experts. However, in many cases, the information is difficult to use for guidance and diagnosis without further interpretation. Image-guided neurosurgery systems (IGNS), for example, have provided a wealth of patient-specific pre-operative images (e.g. MRI, fMRI) to the surgeon during the procedure to use for planning and guidance. However complex brain movements during open-skull operations reduce the effectiveness of using these images for guidance. The objectives of the proposed research program are to develop new probabilistic frameworks in computer vision for a wide range of medical contexts, where they have the potential to lead to concrete improvements in medical diagnosis and patient care. Specifically, we will begin by developing probabilistic medical image registration tools that will permit us to quantify the uncertainties in the resulting interpretation. In the context of neurosurgery, this will permit us to match tracked intra-operative images (e.g. ultrasound) to pre-operative images in order to quantify and correct for brain deformations, and to communicate to the surgeon where the system has confidence in the interpretation. This leads to the first active image-guided neurosurgery system (AIGNS) where visual feedback will permit a surgeon to optimize placement and use of intra-operative imaging systems to reduce ambiguities in the interpretation. Clinical benefits include faster and more accurate surgical procedures, and reductions in patient trauma and hospital costs. We will also apply the framework to breast cancer radiotherapy, with the goal of attaining more accurate radiation treatment of lumpectomy (resected tumour) sites. Finally, this proposal aims to develop new probabilistic frameworks to automatically learn the local statistical variability of image patterns from large datasets of brain images, potentially leading to breakthroughs in our understanding of the natural variability of healthy human brains and to discoveries of new biomarkers of neurological diseases (e.g. Alzheimer's).

医学成像的进步已经允许向医学专家提供大量信息。 然而，在许多情况下，这些信息在没有进一步解释的情况下难以用于指导和诊断。例如，图像引导神经外科系统（IGNS）在手术期间向外科医生提供了大量患者特异性术前图像（例如，MRI、fMRI）以用于规划和引导。然而，在开颅手术中复杂的大脑运动降低了使用这些图像进行指导的有效性。拟议研究计划的目标是为广泛的医疗背景开发计算机视觉中的新概率框架，这些框架有可能导致医疗诊断和患者护理的具体改进。具体来说，我们将开始通过开发概率医学图像配准工具，这将使我们能够量化的不确定性，在由此产生的解释。在神经外科的背景下，这将允许我们将跟踪的术中图像（例如超声）与术前图像进行匹配，以便量化和校正大脑变形，并向外科医生传达系统对解释的信心。 这导致了第一个主动图像引导神经外科系统（AIGNS），其中视觉反馈将允许外科医生优化术中成像系统的放置和使用，以减少解释中的模糊性。临床受益包括更快、更准确的手术程序，以及减少患者创伤和住院费用。我们还将该框架应用于乳腺癌放射治疗，目标是实现对肿块切除术（切除肿瘤）部位更准确的放射治疗。最后，该提案旨在开发新的概率框架，以自动学习来自大脑图像的大型数据集的图像模式的局部统计变异性，这可能导致我们对健康人类大脑的自然变异性的理解取得突破，并发现神经系统疾病（例如阿尔茨海默氏症）的新生物标志物。