The number of digital medical images is growing constantly over the years. This opens new possibilities of extracting information from them using computer-assisted methods, such as artificial intelligence. In this context, the application of radiomics has received increasing attention since 2012. In radiomics, medical image data is exploited by extracting numerous features from them that are not directly visible to the human eye. These features provide valuable information for diagnosis, prognosis and therapy, especially in cancer research. In this paper, we introduce a web-based radiomics module for end users under StudierFenster (www.studierfenster.at), which can extract image features for tumor characterization. StudierFenster is an online, open science medical image processing framework, where multiple clinically relevant modules and applications have been integrated since its initiation in 2018/2019, such as a medical VR viewer and automatic cranial implant design. The newly integrated Radiomics module allows the upload of medical images and segmentations of a region of interest to StudierFenster, where predefined radiomic features are calculated from them using the ‘pyRadiomic’ Python package. The radiomics module is able to calculate not only the basic first-order statistics of the images, but also more advanced features that capture the 2D/3D shape and gray level characteristics. The design of the radiomics module follows the architecture of StudierFenster, where computation-intensive procedures, such as preprocessing of the data and calculating the features for each image-segmentation pair, are executed on a server. The results are stored in a CSV file, which can afterwards be downloaded in a web-based user interface.
KEYWORDS: Augmented reality, Visualization, 3D scanning, 3D displays, Image segmentation, 3D image processing, Medicine, Medical imaging, Data conversion, Computed tomography
This contribution presents a streamlined data pipeline to bring medical 3D scans onto Augmented Reality (AR) hardware. When a 3D scan is visualized on a 2D screen, depth information is lost and doctors have to rely on their experience to map the displayed data to the patient. Showing such a scan in AR addresses this problem, as one can view that scan in real 3D. To achieve this, the scan produced by a medical scanner has to be preprocessed by the user and brought onto the AR hardware. Usually, many manual steps are involved in achieving this, which require technical knowledge about the underlying software and hardware components and impede acceptance of this new technology by the target group, medical personnel. This work presents a streamlined pipeline for this process, leading to an enhanced user experience. The core component of the pipeline is a web application, to which a user can upload the direct output of a medical scanner. The scan can be interactively segmented by the user, after which both the scan and segment are stored on a server. Additionally, this paper introduces an AR application, which can be used to browse through patients and view their scans and previously created segments. We evaluate our streamlined data pipeline and AR application in a user study, reporting the results of a system usability questionnaire and a Thinking Aloud test.
A practical method to analyze blood vessels, like the aorta, is to calculate the vessel's centerline and evaluate its shape in a CT or CTA scan. This contribution introduces a cloud-based centerline tool for the aorta, which computes an initial centerline from a CTA scan with two user given seed points. Afterwards, this initial centerline can be smoothed in a second step. The work done for this contribution was implemented into an existing online tool for medical image analysis, called Studierfenster. In order to evaluate the outcome of this contribution, we tested the smoothed centerline computed within Studierfenster against 40 baseline centerlines from a public available CTA challenge dataset. In doing so, we computed a minimum, maximum, and mean distance between the two centerlines in mm for every data sample, resulting in the smallest distance of 0.59mm, an overall maximum distance of 14.18mm, and a mean distance for all samples of 3.86mm with a standard deviation of 0.99mm.
KEYWORDS: Data conversion, Data communications, Raster graphics, Medicine, Medical imaging, Image processing, Digital imaging, Computer architecture, 3D image processing
Imaging data within the clinical practice in general uses standardized formats such as Digital Imaging and Communications in Medicine (DICOM). Aside from 3D volume data, DICOM files usually include relational and semantic description information. The majority of current applications for browsing and viewing DICOM files online handle the image volume data only, ignoring the relational component of the data. Alternatively, implementations that show the relational information are provided as complete pre-packaged solutions that are difficult to integrate in existing projects and workflows. This publication proposes a modular, client-side web application for viewing DICOM volume data and displaying DICOM description fields containing relational and semantic information. Furthermore, it supports conversion from DICOM data sets into the nearly raw raster data (NRRD) format, which is commonly utilized for research and academic environments, because of its simpler, easily processable structure, and the removal of all patient DICOM tags (anonymization). The application was developed in JavaScript and integrated into the online medical image processing framework StudierFenster (http://studierfenster.tugraz.at/). Since our application only requires a standard web browser, it can be used by everyone and can be easily deployed in any wider project without a complex software architecture.
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