ArticleJournal of medical systems2017
A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Meta-analysis of Survival Prediction in Glioblastoma Patients using Advanced MRI Techniques.Current medical imaging · 2026Pooled it
- Exploring AI-Based System Design for Pixel-Level Protected Health Information Detection in Medical Images.Journal of imaging informatics in medicine · 2026Article
- The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.Scientific reports · 2025Article
- A Method for Efficient De-identification of DICOM Metadata and Burned-in Pixel Text.Journal of imaging informatics in medicine · 2024Article
- Image annotation and curation in radiology: an overview for machine learning practitioners.European radiology experimental · 2024Review
- Anonymizing Radiographs Using an Object Detection Deep Learning Algorithm.Radiology. Artificial intelligence · 2023Article
- OBIA: An Open Biomedical Imaging Archive.Genomics, proteomics & bioinformatics · 2023Article
- A Two-Stage De-Identification Process for Privacy-Preserving Medical Image Analysis.Healthcare (Basel, Switzerland) · 2022Article
- How does DICOM support big data management? Investigating its use in medical imaging community.Insights into imaging · 2021Article
- Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions.Radiology. Artificial intelligence · 2019Article
- Issues and Solutions of Healthcare Data De-identification: the Case of South Korea.Journal of Korean medical science · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
28405948What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.