Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space

摘要 脑积水是脑脊液(CSF)在大脑深处的腔(脑室)中积聚。这个 脑积水最常见的治疗方法是脑室腹膜分流术(VP)脑脊液分流术。完毕 据估计，美国每年有3万例VP分流手术。尽管这种情况很常见 进行手术后，并发症的发生率估计接近24%，其中一份报告引用了22% 修改率。近50%因分流相关问题入院的患者需要住院5天或更长时间。 考虑到手术部位感染和分流探查和翻修相关并发症的比率， 对于许多神经外科医生来说，对分流故障的准确诊断仍然是一个关键的目标，尽管很难实现。其中之一 仅基于成像建立诊断的困难在于缺乏标准化的稳健的方法 测量脑室大小。最近，容量分析被作为一种测量方法进行了研究 脑室大小，与埃文斯指数或额枕角比率相比，已被建议为 更准确、更好地测量脑室大小对分流的反应。然而， 相关的人的努力以及观察者之间和观察者内部在分割脑室时的可变性限制了它的广泛 临床采用。建立脑室巨大或脑积水诊断的另一个困难是， 涉及缺乏标准化的、标准化的数据集，该数据集的范围被认为是各种 随着年龄的增长，脑室大小也随之增大，年龄也不同。目前的文献缺乏可靠的标准化数据集 脑室大小按年龄和性别划分，直到最近才为儿科年龄制作了这样的数据集 射程。在所有年龄段人群中建立脑室容量和形态的标准值 非常需要，并将允许调查与脑积水有关的各种主题，并最终 协助检测和分流脑积水和VP分流相关的并发症或故障。在……里面 近年来，深度学习模型的快速发展对许多领域产生了巨大的影响 医学，特别是用于包括分割在内的自动图像分析任务。利用数字图书馆的优势 模型，本课题提出了两个目标：1)建立并验证了一个健壮的脑室动态力学模型 分段包括多通道支持和自动故障检测，并建立规范的数据库； 2)开发一个融合了DL模型和规范值并适合临床的软件原型 基于图像的分流装置故障诊断工作流程。最终，一个独特的软件产品将是 开发和商业化以提高对分流故障和脑积水的诊断并受益 手术效果好、费用低的患者。

ABSTRACT Hydrocephalus is the buildup of cerebrospinal fluid (CSF) in the cavities (ventricles) deep within the brain. The most common treatment for hydrocephalus is CSF diversion via ventriculoperitoneal (VP) shunting. Over 30,000 VP shunts are placed per year in the United States by some estimates. Despite how commonly this surgery is performed, the complication rate has been estimated at almost 24%, with one report citing a 22% rate of revision. Nearly 50% of patients admitted with shunt related issues require a stay of five or more days. Given the rate of surgical site infections and complications associated with shunt explorations and revisions, accurate diagnosis of a shunt malfunction remains a critical, if elusive, goal for many neurosurgeons. One of the difficulties in establishing a diagnosis based on imaging alone is the lack of standardized robust methods of measuring ventricular size. Recently volumetric analyses have been studied as a method for measuring ventricular size, as compared to the Evans’ Index or frontal-occipital horn ratios and have been suggested is more accurate and a better tool for measuring response of ventricular size to shunting. However, the associated human efforts and inter- and intra-observer variability in segmenting the ventricles prohibits its wide clinical adoption. The other difficulty with establishing a diagnosis of ventriculomegaly or hydrocephalus, involves a lack of a standardized, normative dataset with a range of what is considered "normal" for various age ranges as the ventricle size increases with age. Current literature lacks a robust normative dataset of ventricular size by age and gender and only recently has such a dataset been produced for the pediatric age range. Establishment of normative values for ventricular volume and morphology across all age population is sorely needed and will allow for the investigation of a variety of topics related to hydrocephalus and ultimately assisting in the detection and triage of hydrocephalus and VP shunt related complications or malfunctions. In recent years, the rapid development of deep learning (DL) models has led to great impact on many areas of medicine, especially for automatic image analysis tasks including segmentation. Taking advantage of DL models, two aims are proposed in this project: 1) develop and validate a robust DL model for ventricle segmentation including multi-modality support and automatic failure detection and build a normative database; 2) develop a software prototype that incorporates the DL model and normative values and fits the clinical workflow for image-based diagnosis of shunt malfunction. Ultimately, a unique software product will be developed and commercialized to improve the diagnosis of shunt malfunction and hydrocephalus and benefit the patients with better surgical outcome and reduced cost.