Because of the sensitive nature of information being transmitted, ECN users should attempt to use secure communication methods whenever possible. Non-secure services at Purdue are being disabled in favor of secure services like SSL and SSH. SSH is built into Mac OS X, and is highly configurable. SSH should always be used when connecting to an ECN server.

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All versions of OS X have SSH built in. However, always make sure your computer is up to date by selecting Software Update... from the Apple menu. Occasionally, updates to the SSH protocol and other secure communication protocols are released by Apple.

You can access the SSH command line utility through Terminal.app (found in /Applications/Utilities/Terminal.app). To connect to an ECN server, you will append the command 'ssh' with information about yourself and the server you're connecting to. The command should appear as follows:

ssh -2 -lusername server

The 2 flag forces SSH to connect using SSHv2, a more secure form of SSH. The l (that's a lowercase 'L', not the number '1') flag instructs SSH to use the username following the flag instead of the user executing the SSH command. This can be helpful in ECN labs. Replace username with your ECN user name, and server with the ECN server you wish to connect to. Here's an example of user jsmith connecting to dynamo:

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ssh -2 -l jsmith dynamo.ecn.purdue.edu

There's a simpler way to connect via SSH if you're not concerned about using the SSHv2 flag. Format your command as such:

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ssh jsmith@dynamo.ecn.purdue.edu

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Studierfenster^[1][2] is a free, non-commercial Open Science client/server-based Medical Imaging Processing (MIP) online framework. It offers capabilities, like viewing medical data (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc.) in two-dimensional (2D) and three-dimensional space (3D) directly in a standard web browser, like Google Chrome, Mozilla Firefox, Safari or Microsoft Edge. Other functionalities are the calculation of Medical Metrics (Dice Score^[3] and Hausdorff distance^[4]), manual slice-by-slice outlining of structures in medical images (segmentation^[5][6]), manual placing of (anatomical) landmarks in medical image data, viewing medical data in Virtual Reality (VR) and a facial reconstruction and registration of medical data for Augmented Reality (AR).^[7]

Other features of Studierfenster are the automatic Cranial Implant Design with a neural network,^[8][9] the inpainting of Aortic Dissections^[10] with a Generative Adversarial Network(GAN)^[11][12] and an automatic aortic landmark detection with Deep Learning^[13] in Computed Tomography Angiography (CTA) scans.

Studierfenster is currently hosted on an server at the Graz University of Technology (TU Graz)^[14] in Styria, Austria.

History[edit]

Studierfenster (SF) was initiated within two Bachelor theses during the Summer Bachelor (SB) program of the Institute of Computer Graphics and Vision (ICG) at the Graz University of Technology, Austria, in cooperation with the Medical University of Graz (MedUni Graz), Austria, in 2018/2019.^[15][16]

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 Studierstube (Study Room), which was an Augmented Reality project at the Vienna University of Technology in Austria.^[17][18]

Architecture[edit]

Studierfenster is setup as a distributed application via a client–server model. The client side (front-end) consists of Hypertext Markup Language (HTML) and JavaScript. The front-end also uses the Web Graphics Library (WebGL) that is a Javascript Application Programming Interface (API) descending from the Open Graphics Library (OpenGL) ES 2.0 specification, which it still closely resembles. In contrast to OpenGL, WebGL allows for the rendering of 2D and 3D graphics in web browsers. This enables the use of graphics features known from stand-alone programs directly in web applications, supported by the processing power of the client-side Graphics Processing Unit (GPU).

The server side (back-end) handles client requests via C, C++ and Python.^[19] It interfaces to common Open Source libraries and software tools like the Insight Toolkit (ITK),^[20] the Visualization Toolkit (VTK),^[21] the X Toolkit (XTK)^[22] and Slice:Drop.^[23] The server communication is handled by AJAX requests^[24] were needed.

Studierfenster employs a Flask server. Coincidentally, Flask was created by Armin Ronacher an alumnus of the Graz University of Technology in Austria.^[25]

Features[edit]

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Dicom Browser[edit]

This allows client-side parsing a local folder with DICOM (Digital Imaging and Communications in Medicine^[26][27]) files. Afterwards, the whole folder can be converted to compressed .Nrrd (nearly raw raster data) files and downloaded as a single .zip 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.^[28] With the “Dicom Browser” of Studierfenster, it is possible to select specific Studies or Series, and only convert these.

File Converter[edit]

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

Metrics Module[edit]

This can calculate the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) between two segmentation masks (in .nrrd format) in a standard web browser.

The resulting table has seven columns: the file names for both files used in the calculation, the calculated DSC, the calculated HD, 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 export, a Comma-Separated Values (CSV) file or as a Portable Document Format (PDF).

The Metrics Module has been used to compare manual anatomical segmentations of brain tumors^[29]

VR Viewer[edit]

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).^[30] 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^[31]

Critics[edit]

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

References[edit]

1. ^'Studierfenster'. Retrieved April 23, 2020.
2. ^Weber, Maximilian (October 17, 2019). 'A Client/Server-based Online Environment for the Calculation of Medical Segmentation Scores'. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2019. pp. 3463–3467. doi:10.1109/EMBC.2019.8856481. ISBN978-1-5386-1311-5. PMID31946624. S2CID199373900.
3. ^Dice, Lee R. (1945). 'Measures of the Amount of Ecologic Association Between Species'. Ecology. 26 (3): 297–302. doi:10.2307/1932409. JSTOR1932409.
4. ^Rockafellar, R. Tyrrell; Wets, Roger J-B (2005). Variational Analysis. Springer-Verlag. p. 117. ISBN3-540-62772-3.CS1 maint: discouraged parameter (link)
5. ^Linda G. Shapiro and George C. Stockman (2001): “Computer Vision”, pp 279–325, New Jersey, Prentice-Hall, ISBN0-13-030796-3
6. ^Barghout, Lauren, and Lawrence W. Lee. 'Perceptual information processing system.' Paravue Inc. U.S. Patent Application 10/618,543, filed July 11, 2003.
7. ^Gsaxner, Christina; Pepe, Antonio; Wallner, Jürgen; Schmalstieg, Dieter; Egger, Jan (2019). Shen, Dinggang; Liu, Tianming; Peters, Terry M.; Staib, Lawrence H.; Essert, Caroline; Zhou, Sean; Yap, Pew-Thian; Khan, Ali (eds.). 'Markerless Image-to-Face Registration for Untethered Augmented Reality in Head and Neck Surgery'. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. Lecture Notes in Computer Science. Cham: Springer International Publishing. 11768: 236–244. doi:10.1007/978-3-030-32254-0_27. ISBN978-3-030-32254-0.
8. ^Li, Jianning. 'Deep Learning for Cranial Defect Reconstruction'. Master Thesis, Institute of Computer Graphics and Vision, Graz University of Technology, Austria, pp. 1-68, January 2020.
9. ^Li, Jianning; Pepe, Antonio; Gsaxner, Christina; Egger, Jan (2020). 'An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design'. arXiv:2006.00980 [physics.med-ph].
10. ^Pepe, Antonio; Li, Jianning; Rolf-Pissarczyk, Malte; Gsaxner, Christina; Chen, Xiaojun; Holzapfel, Gerhard A.; Egger, Jan (2020). 'Detection, Segmentation, Simulation and Visualization of Aortic Dissections: A Review'. Medical Image Analysis. 65: 101773. doi:10.1016/j.media.2020.101773. PMID32738647.
11. ^Prutsch, Alexander. 'Design and Development of a Web-based Tool for Inpainting ofDissected Aortae in Angiography Images'(PDF). Retrieved April 25, 2020.
12. ^Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). Generative Adversarial Networks(PDF). Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014). pp. 2672–2680.
13. ^Schmidhuber, J. (2015). 'Deep Learning in Neural Networks: An Overview'. Neural Networks. 61: 85–117. arXiv:1404.7828. doi:10.1016/j.neunet.2014.09.003. PMID25462637. S2CID11715509.
14. ^'Graz University of Technology (TU Graz)'. Retrieved April 28, 2020.
15. ^Weber, Maximilian (December 13, 2018). A Client/Server based Online Environment for the calculation of Segmentation Scores (Bachelor Thesis). Austria: Institute of Computer Graphics and Vision, Graz University of Technology. pp. 1–40.
16. ^Wild, Daniel; Weber, Maximilian; Egger, Jan (2019). 'Client/Server Based Online Environment for Manual Segmentation of Medical Images'. arXiv:1904.08610 [cs.CV].
17. ^'Studierstube'(PDF). Retrieved April 26, 2020.
18. ^Szalavári, Zsolt; Schmalstieg, Dieter; Fuhrmann, Anton; Gervautz, Michael (1998). 'Studierstube: An environment for collaboration in augmented reality'. Virtual Reality, Volume 3. Lecture Notes in Computer Science. Springer International Publishing. 3: 37–48. doi:10.1007/BF01409796. S2CID1122975.
19. ^'Python'. Retrieved April 29, 2020.
20. ^'The Insight Toolkit (ITK)'. Retrieved April 27, 2020.
21. ^'VTK - The Visualization Toolkit'. Retrieved April 27, 2020.
22. ^'The X Toolkit: WebGL™ for Scientific Visualization'. April 25, 2020. Retrieved April 27, 2020.
23. ^'Slice:Drop'. Retrieved April 27, 2020.
24. ^'Ajax - Web developer guides'. MDN Web Docs. Archived from the original on February 28, 2018. Retrieved February 27, 2018.
25. ^'Armin Ronacher'. Retrieved April 26, 2020.
26. ^'1 Scope and Field of Application'. dicom.nema.org.
27. ^DICOM brochure, nema.org.
28. ^Aja-Fernández, Santiago; de Luis Garcia, Rodrigo; Tao, Dacheng; Li, Xuelong (2009). Tensors in Image Processing and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer Science & Business Media. ISBN9781848822993.
29. ^Bhandari, Abhishta; Koppen, Jarrad; Agzarian, Marc (2020). 'Convolutional neural networks for brain tumour segmentation'. Insights into Imaging. Lecture Notes in Computer Science. Springer Open. 11:77 (1): 77. doi:10.1186/s13244-020-00869-4. PMC7280397. PMID32514649.
30. ^Egger, Jan (March 12, 2017). 'HTC Vive MeVisLab integration via OpenVR for medical applications'. PLOS ONE. 12 (3): e0173972. arXiv:1703.07575. Bibcode:2017PLoSO..1273972E. doi:10.1371/journal.pone.0173972. PMC5360258. PMID28323840.
31. ^'ParaView'. Retrieved May 24, 2020.

External links[edit]