Coupled level set framework for retinal segmentation and atlasing in SD-OCT

描述(申请人提供)：一些眼科疾病表现出视网膜的异常或萎缩，这会损害视力并导致失明。传统上被认为只与大脑有关的某些其他疾病，现在已知与视网膜的变化或差异有关，这也会损害视力。光学相干断层扫描(OCT)是一种成像设备，可以实现对视网膜的现代在体高分辨率研究，它正在迅速成为眼科和神经科的一项关键成像技术，眼科的临床诊断和疾病监测正在经历一场戏剧性的技术革命，神经学也是如此，在神经科，脑部疾病的非侵入性视网膜标志物正在开始出现。目前技术的一个关键限制是缺乏对典型的三维光谱域(SD)-OCT检查中整个视网膜观察到的视网膜层进行详细、准确和全自动的分析。尽管工具开始与商业设备联系在一起，但它们目前缺乏复杂性，也没有得到制造商的验证或标准化。这一缺陷对SD-OCT的临床和科学应用都是有问题的，因为测量不具有已知的精度，因此不容易在同一受试者的纵向和人口横截面上进行比较。这项拟议的研究利用了计算机视觉和医学成像中出现的最先进的算法，并将它们应用、调整和扩展到三维视网膜SD-OCT中的层分割的具体应用中。还将专门为视网膜SD-OCT数据开发在归一化空间中进行人口平均的先进工具。这些方法将在正常受试者和经历了主要与视网膜选择性层变薄有关的视网膜变性的患者上进行手动分割和正常化验证。将对患有严重视网膜病变的患者的扫描结果进行评估，如囊肿、脱离或疤痕形成，但预计需要在目前研究基础上进一步发展，以适当地分割和量化此类病例。开发的软件将在开源Java图像科学工具包(JIST)框架内开发，并作为开源软件发布。

DESCRIPTION (provided by applicant): Some eye diseases exhibit abnormalities or atrophy of the retina, which can impair vision and cause blindness. Certain other diseases traditionally thought to be exclusively associated with the brain are now known to be associated with changes or differences in the retina, which can also impair vision. Optical coherence tomography (OCT), an imaging device that is enabling modern in-vivo high-resolution studies of the retina, is rapidly becoming a key imaging technology both in ophthalmology, where clinical diagnoses and disease monitoring are experiencing a dramatic technical revolution, and also in neurology, where noninvasive retinal markers of brain disease are beginning to emerge. A key limitation in the current technology is the lack of detailed, accurate, and fully-automated analysis of the retinal layers that are observed throughout the retina in a typical three-dimensional spectral domain (SD)-OCT exam. Although tools are beginning to appear in association with commercial devices, they are currently lacking in sophistication and are not validated or standardized across manufacturers. This deficiency is problematic for both clinical and scientific application of SD-OCT since the measurements are not of known precision and are therefore not easily compared both in the same subject longitudinally and in population cross sections. The proposed research takes advantage of state-of-the-art algorithms that are emerging in both computer vision and medical imaging and will apply, adapt, and extend them to the specific application of layer segmentation in three-dimensional retinal SD-OCT. Advanced tools for population averaging in a normalized space will also be developed specifically for retinal SD-OCT data. The methods will be validated against manual segmentation and normalization on both normal subjects and patients experiencing retinal degeneration largely associated with thinning of selective layers of the retina. Scans of patients with diseases causing significant retinopathy such as cysts, detachment, or scarring will be evaluated, but further development beyond the present research is expected to be needed to properly segment and quantify such cases. The developed software will be developed within the open-source Java Image Science Toolkit (JIST) framework and released as open source software.