Background: During a deep inferior epigastric perforator (DIEP) flap harvest, the identification and localization of the epigastric arteries and its perforators are crucial. Holographic augmented reality is an innovative technique that can be used to visualize this patient-specific anatomy extracted from a computed tomographic scan directly on the patient. This study describes an innovative workflow to achieve this. Methods: A software application for the Microsoft HoloLens was developed to visualize the anatomy as a hologram. By using abdominal nevi as natural landmarks, the anatomy hologram is registered to the patient. To ensure that the anatomy hologram remains correctly positioned when the patient or the user moves, real-time patient tracking is obtained with a quick response marker attached to the patient. Results: Holographic augmented reality can be used to visualize the epigastric arteries and its perforators in preparation for a deep inferior epigastric perforator flap harvest. Conclusions: Potentially, this workflow can be used visualize the vessels intraoperatively. Furthermore, this workflow is intuitive to use and could be applied for other flaps or other types of surgery.
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. 1 In this context, the application of radiomics has received increasing attention since 2012. 2 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 (http://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 'PyRadiomics' 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.