The Orthanc ecosystem for medical imaging

This paper reviews the components of Orthanc, a free and open-source, highly versatile ecosystem for medical imaging. At the core of the Orthanc ecosystem, the Orthanc server is a lightweight vendor neutral archive that provides PACS managers with a powerful environment to automate and optimize the imaging flows that are very specific to each hospital. The Orthanc server can be extended with plugins that provide solutions for teleradiology, digital pathology, or enterprise-ready databases.

Jodogne, Sébastien. Journal of Digital Imaging, Vol. 31, no.3, p. 341-352 (2018).

On the use of WebAssembly for rendering and segmenting medical images

Rendering medical images is a critical step in a variety of medical applications, from diagnosis to therapy. In this paper, we show how the emerging WebAssembly standard can be used to share the same code base between heavyweight viewers and zero-footprint viewers. Moreover, we introduce a fully functional Web viewer that is entirely developed using WebAssembly and that can be used in research projects or in teleradiology applications. Finally, we demonstrate that deep convolutional neural networks for image segmentation can be executed entirely inside a Web browser thanks to WebAssembly.

Jodogne, Sébastien. Biomedical Engineering Systems and Technologies, Vol. 1814, no.1, p. 393-414 (2023).

Open implementation of DICOM for whole-slide microscopic imaging

This paper introduces an open implementation of DICOM for whole-slide microscopic imaging, following Supplement 145 of the DICOM standard. The software is divided into two parts: (a) a command-line tool to convert an whole-slide image to the DICOM format, and (b) a zero-footprint Web interface to display such DICOM images. The software architecture leverages the DICOM server Orthanc. The entire framework is available as free and open-source software. The existence of this software supports the development of digital pathology and telepathology in clinical environments, featuring a smooth integration with existing EHR and PACS solutions.

Jodogne, Sébastien et al. Proc. of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, p. 81-87 (2017).

Setting a PACS on FHIR

Jodogne, Sébastien. Proc. of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), Vol. 2, p. 123-131 (2024).

Open platform for the de-identification of burned-in texts in medical images using deep learning

Langlois, Quentin et al. Proc. of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), Vol. 1, p. 297-304 (2024).

Interoperable encoding and 3D printing of anatomical structures

Fierens, Amaury et al. ICBRA '23: Proceedings of the 10th International Conference on Bioinformatics Research and Applications, p. 20-26 (2023).

Client-side application of deep learning models through teleradiology

Jodogne, Sébastien. Medical Informatics Europe, Studies in Health Technology and Informatics, Vol. 302, no.1, p. 997-1001 (2023).

Simple, effective deployment of Web viewers for medical imaging in an open platform

Jodogne, Sébastien. EuSoMII Annual Meeting, AI: Connecting the dots (2023).

On the use of DICOM as a storage layer for IIIF

Jodogne, Sébastien. DaSCHCon on IIIF, 3D & Interoperability (2023).

Rendering medical images using WebAssembly

Sébastien Jodogne. Proc. of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (Volume 2), p. 43-51 (2022).

Étude de faisabilité : l'utilisation de l'imagerie médicale en éducation thérapeutique en radiothérapie

Kirkove, Delphine et al. Cancer Radiothérapie, Vol. 26, no. 8, p. 1034-1044 (2022).

Federated learning for heart segmentation

Misonne, Thibaud and Jodogne, Sébastien. IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) (2022).

Automatically publishing medical images from a filesystem as a DICOM server

Insights into Imaging, Vol. 13, no. S2, p. 7 (2021).

Importing and serving open-data medical images to support artificial intelligence research

Insights into Imaging, Vol. 13, no. S2, p. 6 (2021).

Automating DICOM retrieve from PACS with free and open-source software

Annual Congress of the European Association of Nuclear Medicine, European journal of nuclear medicine and molecular imaging, Vol. 45, no. S1, p. 298 (2018).

Automating de-identification and sharing DICOMs with a free and open-source software

Annual Congress of the European Association of Nuclear Medicine, European journal of nuclear medicine and molecular imaging, Vol. 45, no. S1, p. 299 (2018).

Orthanc: A lightweight, RESTful DICOM server for healthcare and medical research

Jodogne, Sébastien et al. Proc. of the IEEE 10th International Symposium on Biomedical Imaging, Vol. 1, no.1, p. 190-193 (2013).