Computational methods for medical image analysis

医学图像分析的计算方法

• 批准号: RGPIN-2018-05636
• 负责人: Savadjiev, Peter
• 金额: $ 1.68万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679189
• 关键词: Computational; methods; medical; image; analysis

The goal of the present grant is to extend the use of computer vision and machine learning methods in medical image analysis for two specific applications: (1) the inference of cardiac muscle fibers from echocardiography imaging, and (2) the image-based characterization and quantification of cancerous tumor appearance. Regarding the first application, the cardiac muscle fibers form the substrate of the cardiac muscle, and as such they are of major importance in cardiology. The ability to infer the geometry of cardiac muscle fibers from echocardiography, the most widely used imaging modality in the clinic, will allow the creation of patient-specific cardiac models to facilitate non-invasive diagnosis and early treatment. With regards to the second application, there has been a dramatic increase in therapeutic options for patients with cancer over the last few years, with the advent of a number of novel targeted drug therapies. However, given the extremely diverse nature of cancer, only a small number of patients are likely to fully benefit from any one particular targeted treatment. It is therefore important to be able to predict, early in the course of treatment, which patients are most likely to benefit from a particular treatment. This can be achieved with non-invasive imaging, together with algorithms for image analysis that can predict response to treatment based on features of tumor appearance.

本基金的目标是将计算机视觉和机器学习方法在医学图像分析中的应用扩展到两个特定的应用：（1）从超声心动图成像中推断心肌纤维，以及（2）基于图像的表征和量化癌性肿瘤外观。关于第一种应用，心肌纤维形成心肌的基底，因此它们在心脏病学中具有重大意义。从超声心动图（临床上最广泛使用的成像方式）推断心肌纤维几何形状的能力将允许创建患者特定的心脏模型，以促进非侵入性诊断和早期治疗。关于第二种应用，在过去几年中，随着许多新型靶向药物疗法的出现，癌症患者的治疗选择急剧增加。然而，鉴于癌症的极端多样性，只有少数患者可能完全受益于任何一种特定的靶向治疗。 因此，重要的是能够在治疗过程的早期预测哪些患者最有可能从特定治疗中受益。这可以通过非侵入性成像以及图像分析算法来实现，该算法可以根据肿瘤外观的特征预测对治疗的反应。