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

虽然吞咽是健康人每天毫不费力地执行数百次的功能，但它是一个极其复杂的过程，涉及人体内众多肌肉和组织的快速和精确协调。在中风（英国每年影响数千人，每年NHS花费23亿英镑）和许多其他神经系统疾病中，吞咽困难（异常吞咽）经常发生，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。对于VF吞咽研究，患者舒适地坐着并给予富含钡（在X射线中变得不透明）的液体。患者吞咽，同时记录头部和颈部的X射线视频图像。该视频必须由临床医生手动检查，但是该任务在视觉上要求很高，非常耗时并且容易出错，因为它需要以慢动作逐帧地重放整个视频并且非常仔细地检查所涉及的解剖区域。这对诊断准确性有直接的影响。我们建议开发一个系统，自动处理整个视频序列，并计算测量结果，这对临床医生的稳健诊断至关重要。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结果，而不是临床医生以慢动作逐帧检查视频数据。我们建议这样做，通过开发图像处理算法，分割图像的组成区域。这些区域将是吞咽期间的指定解剖区域和液体。通过在一定时间内自动跟踪所有这些区域，系统将能够计算临床医生执行诊断所需的所有测量值。在算法开发完成后，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手动估计的测量值进行比较。拟议的工作将对患者，NHS和科学界产生重大利益。这是新颖的，因为没有以前的作品尝试提出的自动化，是及时的，由于目前需要提高基于VF的评价的有效性。

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.