AN AUTOMATED IMAGE ANALYSIS AND MEASUREMENT SYSTEM FOR VIDEO-FLUOROSCOPIC EVALUATION OF SWALLOWING DYSFUNCTIONS.

用于吞咽功能视频透视评估的自动图像分析和测量系统。

• 批准号: EP/E004156/1
• 负责人: John Goulermas
• 金额: $ 24.02万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE004156%2F1
• 关键词: AUTOMATED; IMAGE; ANALYSIS; MEASUREMENT; SYSTEM

Although swallowing is a function performed effortlessly hundreds of times daily by healthy humans, it is an extremely complex process that involves the rapid and precise coordination of numerous muscles and tissues in the human body. In stroke (that affects thousands of people every year in the UK with an annual NHS cost of 2.3 billion) and many other pathologies of the nervous system, dysphagia (abnormal swallowing) frequently occurs and can lead to fatal pneumonia. Video-Fluoroscopy (VF) is the gold-standard for diagnosing dysphagia. For a VF swallowing study, the patient is seated comfortably and given barium enriched (to become opaque in the x-ray) liquid. The patient swallows while x-ray video images of the head and neck are being recorded. This video has to be manually examined by clinicians, but this task is visually demanding, extremely time consuming and error prone, since it requires the replay of the entire video frame-by-frame in slow motion and the very careful examination of the involved anatomical areas. This has direct consequences to the diagnostic accuracy.We propose to develop a system that processes the entire video sequence automatically and calculates measurements that are critical for robust diagnosis by the clinician. Specifically, instead of the clinicians examining the video data frame-by-frame in slow motion, the system will be able to do the job automatically and provide the clinicians with the required measurements. We propose to do this through the development of image processing algorithms that segment an image to its constituent regions. These regions will be the specified anatomical areas and the liquid during swallowing. By automatically tracking all these regions during time, the system will be able to calculate all measurements needed by the clinician to perform a diagnosis. After the algorithms are developed, we plan to apply the system to normal and affected subjects, and compare the automatically calculated measurements with ones estimated manually. The proposed work will have significant benefits on patients, NHS and the scientific community. It is novel as none of the previous works attempted the proposed automation, and is timely due to the current needs for improving the effectiveness of VF-based evaluation.

虽然吞咽是健康人每天毫不费力地完成数百次的功能，但它是一个极其复杂的过程，涉及人体众多肌肉和组织的快速而精确的协调。中风（在英国，每年有成千上万的人受其影响，NHS每年花费23亿美元）和许多其他神经系统疾病，经常发生吞咽困难（吞咽异常），并可能导致致命的肺炎。视频透视（VF）是诊断吞咽困难的金标准。在VF吞咽研究中，患者舒适地坐着，并给予钡剂（在x光片中变得不透明）。病人一边吞咽，一边记录头部和颈部的x光影像。该视频必须由临床医生手动检查，但这项任务对视觉要求很高，非常耗时且容易出错，因为它需要以慢动作逐帧回放整个视频，并非常仔细地检查相关解剖区域。这直接影响到诊断的准确性。我们建议开发一个系统，该系统可以自动处理整个视频序列，并计算对临床医生进行可靠诊断至关重要的测量值。具体来说，该系统将能够自动完成这项工作，并为临床医生提供所需的测量数据，而不是临床医生逐帧检查慢动作视频数据。我们建议通过开发将图像分割到其组成区域的图像处理算法来做到这一点。这些区域将是特定的解剖区域和吞咽时的液体。通过在一段时间内自动跟踪所有这些区域，系统将能够计算临床医生进行诊断所需的所有测量值。算法开发完成后，我们计划将该系统应用于正常和受影响的受试者，并将自动计算的测量值与人工估计的测量值进行比较。拟议的工作将对患者、NHS和科学界有重大好处。它是新颖的，因为以前的工作都没有尝试提出的自动化，并且由于当前需要提高基于vf的评估的有效性，它是及时的。