A unifying approach to machine learning driven medical image segmentation

机器学习驱动的医学图像分割的统一方法

• 批准号: RGPIN-2022-05117
• 负责人: MCINTOSH, CHRIS
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760729
• 关键词: unifying; approach; machine; learning; driven

Medical images contain a plethora of information about patient anatomy and disease. They can be roughly broken into two categories based on intended use: diagnostic, or intervention. Medical image segmentation, or contouring, plays a key role in both medical research and clinical care but is very time consuming and costly. A trained expert uses specialized software to manually delineate the boundary of a structure an image, e.g., the heart in a computed tomography (CT) image. A single CT image is a volume of upwards of 256 image slices with perhaps 10 or more structures to segment in each. In research, segmentation is a vital first step to quantify the shape, and appearance of a structure in an image or series of images over time. However, diagnostic imaging is not routinely contoured for clinical care, thus greatly limiting its research potential without expensive contouring. For example, to answer a seemingly simple question, does COVID-19 cause a change in the shape of the heart or lungs in patients pre-and post- infection across a dataset of 1,000 patients could require upwards of 256,000 contours and 700 hours of expert labor (assuming 10 seconds per slice) creating a significant barrier to larger studies with tens of thousands of patients. To unlock the full research potential of Canada's vast repositories of diagnostic medical images automated segmentation technologies are required. Segmentation can also play a key role in clinical care. In diagnostic images automated segmentation could be used to better quantify diseased areas. In contrast, many medical procedures (for example radiation therapy for cancer patients) require segmentation for intervention planning. In these clinical workflows automated segmentation could improve efficiency enabling greater throughput without a cost increase, benefiting Canadians through reduced wait times and reduced healthcare system costs. To date due to technical limitations medical image segmentation methods have been limited to training on purpose-built research datasets consisting of a few hundred patients, with a heavy limit on the number of possible structures, typically 9 or fewer for a given model. We propose to advance state-of-the-art methodologies for medical image segmentation by increasing their generalizability to building models from thousands of routine clinical radiation therapy images, and more than 77 distinct structures. This research will not only advance knowledge in medical image segmentation methodologies but unlock the greater potential of datasets in Canada and develop more robust segmentation models brining us closer to a solved solution for segmentation to use in both research and clinical workflows.

医学图像包含了过多的关于患者解剖和疾病的信息。根据预期用途，它们可以大致分为两类：诊断或干预。医学图像分割或轮廓化在医学研究和临床护理中都发挥着关键作用，但非常耗时和昂贵。训练有素的专家使用专门的软件来手动描绘图像中的结构的边界，例如计算机断层扫描(CT)图像中的心脏。一个CT图像是由256个以上的图像切片组成的体积，每个切片中可能有10个或更多的结构要分割。在研究中，分割是量化一幅或一系列图像中结构随时间变化的形状和外观的重要第一步。然而，诊断成像并不是常规的用于临床护理的轮廓，因此在没有昂贵的轮廓的情况下极大地限制了其研究潜力。例如，要回答一个看似简单的问题，新冠肺炎是否会导致患者在感染前和感染后的心脏或肺的形状发生变化？在一个包含1000名患者的数据集中，可能需要超过25.6万条轮廓和700小时的专家劳动(假设每片10秒)，这为涉及数万名患者的大型研究制造了巨大的障碍。为了充分挖掘加拿大庞大的诊断医学图像库的研究潜力，需要使用自动分割技术。分割也可以在临床护理中发挥关键作用。在诊断图像中，自动分割可以用来更好地量化病变区域。相比之下，许多医疗程序(例如癌症患者的放射治疗)需要分割以进行干预计划。在这些临床工作流程中，自动化分割可以提高效率，在不增加成本的情况下实现更大的吞吐量，通过减少等待时间和降低医疗系统成本使加拿大人受益。到目前为止，由于技术限制，医学图像分割方法仅限于在由数百名患者组成的专门构建的研究数据集上进行训练，对可能的结构数量有很大限制，对于给定的模型，通常是9个或更少。我们建议推进最先进的医学图像分割方法，通过增加它们的泛化能力来从数千个常规的临床放射治疗图像和超过77个不同的结构中建立模型。这项研究不仅将促进医学图像分割方法的知识，还将释放加拿大数据集的更大潜力，并开发更稳健的分割模型，使我们更接近于在研究和临床工作流程中使用的分割解决方案。