使用 Pylogik 进行医学图像去标识化、清洗和压缩
Medical Image Deidentification, Cleaning and Compression Using Pylogik
AI总结：
PyLogik 是一个 Python 库，通过文本检测、滤波、阈值处理等流程对超声图像进行去标识化、清洗和压缩，Dice 系数达 0.976，文件平均缩小 72%，促进数据共享与深度学习应用。
摘要：
在大数据和机器学习时代利用医疗记录信息，前提是数据必须经过清洗和去标识化处理。当受保护的健康信息（PHI）包含或嵌入在图像元数据中时，促进多中心合作的数据共享和协调尤其困难。我们提出一个名为 PyLogik 的 Python 框架中的新型库，以帮助缓解超声图像的这一问题，由于 PHI 经常直接包含在图像上，超声图像的处理尤其具有挑战性。PyLogik 通过一系列文本检测/提取、滤波、阈值处理、形态学和轮廓比较来处理图像体数据。该方法对图像进行去标识化，减小文件大小，并为深度学习应用和数据共享准备图像体数据。为了评估其在处理超声数据方面的有效性，随机抽取了 50 例心脏超声（超声心动图）样本通过 PyLogik 处理，并将输出结果与专家用户的手动分割结果进行比较。两种方法的 Dice 系数平均值为 0.976。接下来，进行了一项调查以确定使用该算法实现的信息压缩程度。经 PyLogik 处理后，结果数据平均约缩小 72%。我们的结果表明，PyLogik 是一种可行的数据清洗和去标识化、确定 ROI 以及文件压缩的方法，这将有助于超声数据的高效存储、使用和传播。该流程的变体也已创建，用于其他医学成像数据类型。
英文摘要：
Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are particularly difficult when protected health information (PHI) is contained or embedded in image meta-data. We propose a novel library in the Python framework, called PyLogik, to help alleviate this issue for ultrasound images, which are particularly challenging because of the frequent inclusion of PHI directly on the images. PyLogik processes the image volumes through a series of text detection/extraction, filtering, thresholding, morphological and contour comparisons. This methodology de-identifies the images, reduces file sizes, and prepares image volumes for applications in deep learning and data sharing. To evaluate its effectiveness in processing ultrasound data, a random sample of 50 cardiac ultrasounds (echocardiograms) were processed through PyLogik, and the outputs were compared with the manual segmentations by an expert user. The Dice coefficient of the two approaches achieved an average value of 0.976. Next, an investigation was conducted to ascertain the degree of information compression achieved using the algorithm. Resultant data was found to be on average ~72% smaller after processing by PyLogik. Our results suggest that PyLogik is a viable methodology for data cleaning and de-identification, determining ROI, and file compression which will facilitate efficient storage, use, and dissemination of ultrasound data. Variants of the pipeline have also been created for use with other medical imaging data types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
