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Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project

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Abstract

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.

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Data Availability

The datasets used during and/or analyzed during the current study are not available; they belong to the producer/host center: CLB. The data used in this study adhere to the tenets of the Declaration of Helsinki.

Notes

  1. https://crowds-cure.org.

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Funding

This work was supported by the CNRS via the INS2I single call. This work was performed within the framework of the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program "Investissements d'Avenir" operated by the French National Research Agency (ANR).

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Authors and Affiliations

  1. INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, F-69XXX, France

    Tiphaine Diot-Dejonghe, Benjamin Leporq, Amine Bouhamama, Helene Ratiney, Frank Pilleul, Olivier Beuf & Frederic Cervenansky

  2. Department of Radiology, Centre Léon Bérard, 28 Prom. Léa et Napoléon Bullukian, Lyon, 69008, France

    Amine Bouhamama & Frank Pilleul

Authors
  1. Tiphaine Diot-Dejonghe
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  2. Benjamin Leporq
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Contributions

All authors contributed to the study conception. Tiphaine Diot and Frederic Cervenansky contribute to the design of AWESOMME. Data collection and analysis were performed by Amine Bouhamama and Benjamin Leporq. The first draft of the manuscript was written by Tiphaine Diot and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Frederic Cervenansky.

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This is a technical study. The CLB Research Ethics Committee has confirmed that no ethical approval is required.

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Informed consent was obtained from all individual participants included in the study.

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The authors affirm that human research participants provided informed consent for publication of the images in Fig. 6.

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Diot-Dejonghe, T., Leporq, B., Bouhamama, A. et al. Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01110-0

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