TOPIC #411 - PHASE I SBIR CONTRACT - DE-IDENTIFICATION SOFTWARE TOOLS FOR CANCER IMAGING RESEARCH

为医学成像应用开发人工智能技术需要在大型和多样化的数据集上训练模型。 目前，包括放射学和病理学图像在内的大型数据存储库的聚合受到患者隐私问题的限制。 为了成功共享医学图像，机构必须能够快速准确地批量去识别大量图像。 这一过程目前是手工进行的，而且很耗时。 我们提出了一个管道，通过利用机器学习，自然语言处理和分区工作流技术，以显着减少匿名医学图像所需的人为干预，从放射学DICOM图像和病理学全切片图像中删除PHI。 除了检查图像中的标题数据外，我们还将使用光学字符识别和计算机视觉算法来检测图像中任何位置或方向的文本，然后自动记录并随后清除这些区域。这些技术将被配置为适用于各种图像类型（CT、MRI、X光片等），并涵盖放射学和病理学图像的多个OEM供应商。 第一阶段工作说明书将构建必要的软件工具、方法和数据集，以促进第二阶段的工作，在第二阶段，将开发自主去识别所需的复杂算法。 在本文件中，第二阶段的处理过程称为工作流程。

Developing artificial intelligence technology for medical imaging applications requires training models on large and diverse datasets. Currently, aggregation of large data repositories, including radiology and pathology images, is limited by concerns around patient privacy. In order to successfully share medical images, an institution must be able to quickly and accurately de-identify large numbers of images in batches. This process is currently manual and time-consuming. We propose a pipeline to remove PHI from both radiology DICOM images and pathology whole slide images by leveraging machine learning, natural language processing, and compartmentalized workflow techniques to significantly reduce the human intervention needed to anonymize medical images. In addition to examining header data in the images, we will use optical character recognition and computer vision algorithms to detect text in any location or orientation in the image, then automatically record and subsequently purge these regions. These techniques will be configured to work on a variety of image types (CT, MRI, radiograph, etc) and cover multiple OEM vendors for both radiology and pathology images. This phase I statement of work will construct the software tools, methods, and datasets necessary to facilitate a phase II where the complex algorithms needed for autonomous deidentification will be developed. This phase II processing will be referred to throughout this document as the workflow.