Studierfenster Explained

StudierFenster
Logo Caption:The Studierfenster Logo
Developer:University Hospital Essen, Graz University of Technology, Medical University of Graz
Language:English
Operating System:Cross-platform
(Windows, Mac OS X, Linux)
Programming Language:C, C++, Python, JavaScript, HTML
Genre:Image processing, scientific visualization, medical imaging, volume rendering, Interactive visualization
License:GPL, CC BY-SA

Studierfenster or StudierFenster (SF)[1] [2] [3] is a free, non-commercial open science client/server-based medical imaging processing online framework. It offers capabilities, like viewing medical data (computed tomography (CT), magnetic resonance imaging (MRI), etc.) in two- and three-dimensional space directly in the standard web browsers, like Google Chrome, Mozilla Firefox, Safari, and Microsoft Edge. Other functionalities are the calculation of medical metrics (dice score[4] and Hausdorff distance[5]), manual slice-by-slice outlining of structures in medical images (segmentation[6] [7]), manual placing of (anatomical) landmarks in medical image data, viewing medical data in virtual reality, a facial reconstruction and registration of medical data for augmented reality,[8] one click showcases for COVID-19 and veterinary scans, and a Radiomics module.

Other features of Studierfenster are the automatic cranial implant design with a neural network,[9] [10] the inpainting of aortic dissections[11] with a generative adversarial network,[12] [13] an automatic aortic landmark detection with deep learning[14] in computed tomography angiography scans, and a GrowCut algorithm implementation for image segmentation.

Studierfenster is currently hosted on a server at the Graz University of Technology[15] in Austria, and expanded jointly with the Institute for Artificial Intelligence in Medicine (IKIM)[16] in Essen, Germany.

History

Studierfenster was initiated within two bachelor theses during the summer bachelor program of the Institute of Computer Graphics and Vision (ICG) at Graz University of Technology, Austria, in cooperation with the Medical University of Graz, Austria, in 2018/2019.[17] [18]

The name Studierfenster (or StudierFenster) is German and can be translated to 'StudyWindow', whereby window refers here to a browser window. The word Studierfenster is an adaption from the word ('study room'), which was an augmented reality project at the Vienna University of Technology in Austria.[19] [20]

Architecture

Studierfenster is set up as a distributed application via a client–server model. The client side (front-end) consists of HTML and JavaScript with WebGL to enable 2D and 3D visualization, rendered on the client.

The server side (back-end) handles client requests via C, C++ and Python.[21] It interfaces to common open source libraries and software tools like the Insight Toolkit,[22] the Visualization Toolkit (VTK),[23] the X Toolkit (XTK)[24] and Slice:Drop.[25] The server communication is handled by AJAX requests[26] were needed.

Studierfenster employs a Flask server.

Features

Dicom browser

This allows client-side parsing a local folder with DICOM (Digital Imaging and Communications in Medicine)[27] [28] files. Afterwards, the whole folder can be converted to compressed (nearly raw raster data) files and downloaded as a single file.

Nrrd is a library and file format for the representation and processing of n-dimensional raster data. It is intended to support scientific visualization and (medical) image processing applications.[29] With the "Dicom Browser" of Studierfenster, it is possible to select specific Studies or Series, and only convert these.

File converter

The file converter converts a medical volume file (e.g. a non-compressed file) to a compressed/binary file. After the conversion, the compressed file can be downloaded and used with the "Medical 3D Viewer" for 2D and 3D visualization, and further image processing.

Metrics module

This can calculate the Dice similarity coefficient and Hausdorff distance between two segmentation masks (in format) in a standard web browser.

The resulting table has seven columns: the file names for both files used in the calculation, the calculated Dice similarity coefficient, the calculated Hausdorff distance, the calculated directed HD for both directions, and the information if image spacing was used in the calculation. The table can be sorted, is searchable, and can be exported as a simple copy, an Excel spreadsheet, a comma-separated values file or as a portable document format.

The Metrics Module has been used to compare manual anatomical segmentations of brain tumors[30]

VR viewer

The VR Viewer (or Medical VR Viewer) enables viewing (medical) data in Virtual Reality (VR) with devices like the Google Cardboard or the HTC Vive (via the WebVR App).[31] For viewing the data in VR, it needs to be converted to the VTI (.vti) format, which can be done with open-source, multi-platform data analysis and visualization application ParaView[32]

Critics

Studierfenster is not a certified medical product; it can only be used for educational, research, and informational purposes.

External links

Notes and References

  1. Web site: Studierfenster. 2020-04-23.
  2. Egger. Jan. Wild. Daniel. Weber. Maximilian. Ramirez Bedoya. Christopher. Karner. Florian. Prutsch. Alexander. Schmied. Michael. Dionysio. Christina. Krobath. Dominik. Jin. Yuan. Gsaxner. Christina. Li. Jianning. Pepe. Antonio. 2022. Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform. Journal of Digital Imaging. 35 . 2 . en. Cham. Springer International Publishing. 340–355. 10.1007/s10278-021-00574-8. 35064372. 8782222.
  3. Book: Weber, Maximilian. A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores . 199373900. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019-10-17. 2019. 3463–3467. 10.1109/EMBC.2019.8856481. 31946624. 978-1-5386-1311-5. en-US.
  4. Dice . Lee R. . Measures of the Amount of Ecologic Association Between Species . 1932409 . Ecology . 26 . 3 . 1945 . 297–302 . 10.2307/1932409 . 1945Ecol...26..297D . 53335638 .
  5. Book: R. Tyrrell Rockafellar

    . R. Tyrrell Rockafellar . R. Tyrrell . Rockafellar . Roger J-B Wets . Roger J-B . Wets . Variational Analysis . Springer-Verlag . 2005 . 3-540-62772-3 . 117 .

  6. [Linda Shapiro|Linda G. Shapiro]
  7. Barghout, Lauren, and Lawrence W. Lee. "Perceptual information processing system." Paravue Inc. U.S. Patent Application 10/618,543, filed July 11, 2003.
  8. Book: Gsaxner. Christina. Pepe. Antonio. Wallner. Jürgen. Schmalstieg. Dieter. Egger. Jan. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery . 2019. Shen. Dinggang. Liu. Tianming. Peters. Terry M.. Staib. Lawrence H.. Essert. Caroline. Zhou. Sean. Yap. Pew-Thian. Khan. Ali. Lecture Notes in Computer Science. 11768. en. Cham. Springer International Publishing. 236–244. 10.1007/978-3-030-32254-0_27. 978-3-030-32254-0. 202620139.
  9. Deep Learning for Cranial Defect Reconstruction. Li. Jianning. Master Thesis, Institute of Computer Graphics and Vision, Graz University of Technology, Austria, pp. 1-68, January 2020.. January 2020. Master's Thesis.
  10. Book: 2006.00980. Li. Jianning. Pepe. Antonio. Gsaxner. Christina. Egger. Jan. Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling . An online platform for automatic skull defect restoration and cranial implant design . Cristian A. . Jeffrey H. . Linte . Siewerdsen . Medical Imaging 2021: Image-Guided Procedures . 2021. 11598 . 59 . 10.1117/12.2580719 . 2021SPIE11598E..1QL . 9781510640252 . 219177447 .
  11. 10.1016/j.media.2020.101773. Detection, Segmentation, Simulation and Visualization of Aortic Dissections: A Review. 2020. Pepe. Antonio. Li. Jianning. Rolf-Pissarczyk. Malte. Gsaxner. Christina. Chen. Xiaojun. Holzapfel. Gerhard A.. Egger. Jan. Medical Image Analysis. 65. 101773. 32738647. 220924735.
  12. Web site: Design and Development of a Web-based Tool for Inpainting ofDissected Aortae in Angiography Images. Prutsch. Alexander. 2020-04-25.
  13. Generative Adversarial Networks. Ian . Goodfellow . Jean . Pouget-Abadie . Mehdi . Mirza . Bing . Xu . David . Warde-Farley . Sherjil . Ozair . Aaron . Courville . Yoshua . Bengio . Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014) . 2672–2680 . 2014.
  14. Schmidhuber. J.. 11715509. 2015. Deep Learning in Neural Networks: An Overview. Neural Networks. 61. 85–117. 1404.7828. 10.1016/j.neunet.2014.09.003. 25462637.
  15. Web site: Graz University of Technology. 2020-04-28.
  16. Web site: Artificial Intelligence in Medicine. 2022-02-09.
  17. A Client/Server based Online Environment for the calculation of Segmentation Scores. Weber. Maximilian. 2018-12-13. Bachelor Thesis . Institute of Computer Graphics and Vision, Graz University of Technology . Austria . 1–40.
  18. 1904.08610. Wild. Daniel. Weber. Maximilian. Egger. Jan. Client/Server Based Online Environment for Manual Segmentation of Medical Images. 2019. cs.CV.
  19. Web site: Studierstube. 2020-04-26.
  20. Szalavári. Zsolt. Schmalstieg. Dieter. Fuhrmann. Anton. Gervautz. Michael. ?Studierstube?: An environment for collaboration in augmented reality . 1122975. 1998. Virtual Reality . Lecture Notes in Computer Science. 3. en. Springer International Publishing. 37–48. 10.1007/BF01409796.
  21. Web site: Python. 2020-04-29.
  22. Web site: The Insight Toolkit. 2020-04-27.
  23. Web site: VTK - The Visualization Toolkit. 2020-04-27.
  24. Web site: The X Toolkit: WebGL™ for Scientific Visualization. GitHub. 25 April 2020. 2020-04-27.
  25. Web site: Slice:Drop. 2020-04-27.
  26. Web site: Ajax - Web developer guides. MDN Web Docs. 2018-02-27. https://web.archive.org/web/20180228161512/https://developer.mozilla.org/en-US/docs/Web/Guide/AJAX. 28 February 2018. live.
  27. Web site: 1 Scope and Field of Application. dicom.nema.org.
  28. http://medical.nema.org/dicom/geninfo/Brochure.pdf DICOM brochure
  29. Book: Tensors in Image Processing and Computer Vision . Aja-Fernández . Santiago . de Luis Garcia. Rodrigo . Tao . Dacheng. Li. Xuelong . 9781848822993 . Advances in Computer Vision and Pattern Recognition . 2009 . Springer Science & Business Media .
  30. Bhandari. Abhishta. Koppen. Jarrad. Agzarian. Marc. 2020. Convolutional neural networks for brain tumour segmentation. Insights into Imaging. Lecture Notes in Computer Science. 11:77. 1. 77. en. Springer Open. 10.1186/s13244-020-00869-4. 32514649. 7280397 . free .
  31. HTC Vive MeVisLab integration via OpenVR for medical applications. Egger. Jan. PLOS ONE. 2017-03-12. 12. 3. e0173972. 10.1371/journal.pone.0173972. 28323840. 5360258. 1703.07575. 2017PLoSO..1273972E. en-US. free.
  32. Web site: ParaView. 2020-05-24.

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