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

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.

Keywords: Deep learning; Girder; Interactive image segmentation; Medical imaging; OHIF; Radiomics; Web-viewer.

Fig. 1

Illustration of the processing stages of medical image analysis: from acquisition to data mining for computer-aided diagnosis

Fig. 2

Overview of the architecture of the AWESOMME Platform. Three main components are visible, for each new plugins and communication protocol for user traceability were developed

Fig. 3

Check-in workflow to access data

Fig. 4

New functionalities on the page. Two modification categories are displayed: the new filtering selector shown in red and new buttons in the header part to access new functionalities forms shown in green at the top

Fig. 5

Batch analysis forms for segmentation (left) and radiomics extraction (right)

Fig. 6

Enhanced OHIF: shows the global view with data visualization and tools with the new segmentation panel

Fig. 7

Enhanced OHIF: (left) only shows the radiomics panel, it is accessible the same way as the segmentation panel on the side on the window. (middle) shows the side panel for launching diagnostic. (right) Presentation of the scoring panel. One can select a type of score, and then the chosen score. Here it uses a scale of pre-defined values but for other cases a text box can display. A physician can enter the confidence in their scoring and add a comment before saving it to the Girder server