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Fundus Analysis Software Tool (FAST): development of software integrating CAD with the EHR for the longitudinal study of fundus images

机译:USFUS分析软件工具(快速):开发软件与EHR集成CAD的纵向研究对眼底图像的纵向研究

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In this paper, we present the previous development and deployment of Fundus Analysis Software Tool (FAST) to enable the analysis of different anatomical features and pathologies within fundus images over time, and demonstrate its usefulness with three use cases. First, we utilized FAST to acquire 616 fundus images from a remote clinic in a HIPAAcompliant manner. An ophthalmologist at the clinic then used FAST to annotate 190 fundus images containing exudates at the pixelwise level in a time-efficient manner. In comparison with publicly available datasets, our dataset constitutes the largest pixelwise-labeled collection of images and the first exudate segmentation dataset with eye-matched pairs of images for a given patient. Second, we developed an optic disk CAD segmentation algorithm, where our algorithm achieved a mean intersection over union of 0.930, comparable to the disagreement between ophthalmologist annotations. We deployed this algorithm into FAST, where it segments and flushes the segmentation onto the computer screen while simultaneously filling out specified optic disk fields of a DICOM-SR report on the fundus image. Third, we integrated our software with the open-source EHR framework OpenMRS, where our software can upload both automatic and manual analyses of the fundus to a remote server using HL7 FHIR standard then retrieve historical reports for a patient chronologically. Finally, we discuss our design decisions in developing FAST, particularly those relating to its treatment of DICOM-SR reports based on fundus images and its usage of the FHIR standard and its next steps towards enabling effective analyses of fundus images.
机译:在本文中,我们提出了先前的开发和部署基底分析软件工具(FAST),以便随着时间的推移分析眼底图像中的不同解剖特征和病理学,并展示其具有三种用例的有用性。首先,我们利用快速从远程诊所获得616个眼底图像,以HiPaAcomplant方式。诊所的眼科医生随后使用快速注释含有在像素水平的渗出物的190个眼底图像,以时效的方式。与公开可用的数据集相比,我们的数据集构成了具有用于给定患者的眼睛匹配的图像的最大像素标记的图像和第一举射分段数据集。其次,我们开发了一种光盘CAD分割算法,其中我们的算法达到了0.930的联盟的平均交叉点,与眼科医学家注释之间的分歧相当。我们将此算法部署到快速,其中它段并将分段刷新到计算机屏幕上,同时填写在眼底图像上的DICOM-SR报告的指定视镜磁盘字段。第三,我们将我们的软件与开源EHR框架OpenMRS集成,我们的软件可以使用HL7 FHIR标准将基底的自动和手动分析上传到远程服务器,然后按顺序检索患者的历史报告。最后,我们讨论了我们在开发快速发展方面的设计决策,特别是根据其基于眼底图像及其利用FHIR标准及其利用迈出了有效分析对底图像的后续步骤的DICOM-SR报告。

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