Orthanc - A lightweight, restful DICOM server for healthcare and medical research

Jodogne, Sébastien; Bernard, Chrystelle; Devillers, Magali; Lenaerts, Eric; Coucke, Philippe

doi:10.1109/isbi.2013.6556444

Cited by 27 publications

(11 citation statements)

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“…using MIRC CTP [ 14 ]. Orthanc will be used to initiate image transfer via the DICOM WADO RESTful interfaces [ 15 ]. Patient identifying data will be removed from narratives using site- and context-specific information about the structure and content of documents.…”

Section: Architectural Framework and Methodologymentioning

confidence: 99%

“…Technically, the data lake will contain different systems for managing structured data, clinical narratives (notes, letters, reports), images and omics data. Examples of storage solutions that will be used include PostgreSQL databases, OpenBIS [ 17 ], a variant store and a PACS [ 15 , 18 ]. The services provided in the working environment will consist of different solutions for processing the different types of data managed by the DIC.…”

Section: Architectural Framework and Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Data Integration for Future Medicine (DIFUTURE)

Praßer¹,

Kohlbacher²,

Mansmann³

et al. 2018

Methods Inf Med

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Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagno...

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Section: Architectural Framework and Methodologymentioning

confidence: 99%

Section: Architectural Framework and Methodologymentioning

confidence: 99%

Data Integration for Future Medicine (DIFUTURE)

Praßer¹,

Kohlbacher²,

Mansmann³

et al. 2018

Methods Inf Med

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“…PET/MRI datasets were imported into the Fiji open source image processing package [14], using the ImageJ interface to the Orthanc DICOM server [15]. The primary gross tumor volume (GTV) was manually outlined by two experienced radiologists in consensus using Fiji on sagittal VIBE MR sequences 20 seconds after i. v. contrast agent injection.…”

Section: Tumor Segmentationmentioning

confidence: 99%

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

Umutlu

Nensa

Demircioğlu

et al. 2020

Rofo

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Zielsetzung Ziel dieser Studie war die Evaluierung des prädiktiven Potenzials der Radiomics-Analyse zur Bestimmung des N- und M-Stadiums des primären Zervixkarzinoms anhand multiparametrischer 18F-FDG-PET/MRT-Bildgebung. Material und Methoden 30 Patientinnen mit einem histologisch gesicherten, primären und therapienaiven Zervixkarzinom unterzogen sich einer multiparametrischen 18F-FDG-PET/MRT-Untersuchung unter Verwendung eines dedizierten Untersuchungsprotokolls des weiblichen Beckens. Nach Segmentierung der Primärtumoren wurden quantitative Bildparameter mittels der Radiomic-Image-Processing-Toolbox bestimmt. Insgesamt wurden 45 verschiedene quantitative Bildmerkmale jeweils anhand der T2-gewichteten TSE-Sequenzen, der nativen und kontrastmittelgestützten T1-gewichteten TSE-Sequenzen, der ADC-Map, verschiedenen Perfusionsparametern (Ktrans, Kep, Ve and iAUC) und den 18F-FDG-PET-Datensätzen für jeden Tumor extrahiert. Die statistische Analyse zur Bestimmung des N- und M-Stadiums erfolgte unter der Verwendung der Python 3.5 und Scikit-learn-Software-Bibliothek für maschinelles Lernen. Ergebnisse Insgesamt zeigte sich eine höhere Genauigkeit zur Prädiktion des korrekten M-Stadiums im Vergleich zum N-Stadium. Zur Prädiktion des korrekten M-Stadiums zeigten sich unter der Verwendung von SVM und SVM-RFE zur Feature-Auswahl die besten Ergebnisse mit einer Sensitivität von 91 %, einer Spezifität von 92 % und einer Fläche unter der Kurve (AUC) von 0,97. Die höchste Genauigkeit für die Bestimmung des N-Stadiums erfolgte unter der Verwendung von RBF-SVM und MIFS zur Feature-Auswahl mit einer Sensitivität von 83 %, einer Spezifität von 67 % und einer Fläche unter der Kurve (AUC) von 0,82. Schlussfolgerung Die Radiomics-Analyse von multiparametrischen PET/MR-Datensätzen ermöglicht eine präzise Prädiktion des M- und N-Stadiums von Patientinnen mit primärem Zervixkarzinom und könnte damit supportiv zur nichtinvasiven Tumor-Phänotypisierung und Patientenstratifizierung eingesetzt werden. Kernaussagen: Citation Format

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“…An optional feature which enables radiologists to view of DICOM scans (in the PACS server) over the Internet. The content may be viewed though a web browser (e.g., medDream and Orthanc [41]), an app on a mobile device (e.g., FujiFilm's Synapse Mobility), or accessed via a web API (e.g., Dicoogle [42]).…”

Section: Network Topologymentioning

confidence: 99%

CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

Mirsky,

Mahler,

Shelef

et al. 2019

Preprint

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In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services. An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market.In this paper, we show how an attacker can use deeplearning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. We implement the attack using a 3D conditional GAN and show how the framework (CT-GAN) can be automated. Although the body is complex and 3D medical scans are very large, CT-GAN achieves realistic results which can be executed in milliseconds.To evaluate the attack, we focused on injecting and removing lung cancer from CT scans. We show how three expert radiologists and a state-of-the-art deep learning AI are highly susceptible to the attack. We also explore the attack surface of a modern radiology network and demonstrate one attack vector: we intercepted and manipulated CT scans in an active hospital network with a covert penetration test.

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Orthanc - A lightweight, restful DICOM server for healthcare and medical research

Cited by 27 publications

References 1 publication

Data Integration for Future Medicine (DIFUTURE)

Data Integration for Future Medicine (DIFUTURE)

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

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