A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format
| Autor: | Eriksson Monteiro, Carlos Costa, José Luís Oliveira |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
020205 medical informatics
Computer science Medicine (miscellaneous) Health Informatics Image processing 02 engineering and technology computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences DICOM 0302 clinical medicine Health Information Management Data Anonymization Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering Ultrasonography Information retrieval Information Dissemination business.industry Deep learning De-identification Pipeline (software) Data sharing Information sensitivity Workflow Privacy Data mining Artificial intelligence business computer Confidentiality Software Information Systems |
| Zdroj: | Journal of Medical Systems. 41 |
| ISSN: | 1573-689X 0148-5598 |
| DOI: | 10.1007/s10916-017-0736-1 |
| Popis: | Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community. |
| Databáze: | OpenAIRE |
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