A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.
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 informa...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 5; pp. 1 - 17 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=122782821&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122782821 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2017 vid: 41 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 122782821 122782821 122782821 10.1007/s10916-017-0736-1 122782821 ppf: 1 ppct: 16 formats: fmt: @attributes: type: P tig: atl: A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format. aug: au: Monteiro, Eriksson Costa, Carlos Oliveira, José affil: University of Aveiro, DETI/IEETA , Aveiro Portugal sug: subj: DICOM Ultrasonography Clinical Information Systems Product Evaluation Software Privacy and Confidentiality Electronic Health Records Health Insurance Portability and Accountability Act Models, Structural Algorithms Human Artificial Intelligence Classification Funding Source Descriptive Statistics Data Analysis Software ab: 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 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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