Resources for Research in Computer Assisted Surgery

Survey papers and books

Journals and conferences

Who, Where

Software, Clinical Data, Hardware

Research in Computer Assisted Surgery (CAS) includes all algorithmic and technological subjects involved in systems providing physicians with real-time guidance during medical interventions. This page includes references to survey papers on the technological aspects of CAS, links to journals and relevant conferences. You can also find links to resources for CAS related research. Most of these are freely available online.

Other acronyms for CAS are:

• Computer Integrated Surgery (CIS).
• Computer Aided Intervention (CAI).
• Image Guided Therapy (IGT).
• Image Guided Surgery (IGS).
• Image Guided Interventions (IGI).
• Technology Guided Therapy (TGT).

Before you go any further

When working with clinical images you need to ensure that your work is either exempted from federal regulations on human subjects research or that you obtain institutional review board (IRB) permission. This tool from NIH can help you determine whether your proposed work is exempted from these regulations.

Clinical data contains information that uniquely identifies the patient. When working with images we need to ensure that this data is not exposed unnecessarily. Handle the data the same way you would like your own medical records handled.

Ideally you anonymize all data before it gets to your lab. When working with DICOM, the format from hell, anonymization usually involves removal of meta-data information. This is sufficient in most cases but is potentially problematic when the data are 3D cranial scans such as the one shown alongside the picture below. While for some wearing glasses may be enough to disguise a persons identity, most often this is not the case.

This issue was (re)identified in multiple publications over the past two decades (papers listed in chronological order):

1. "A technique for the deidentification of structural brain MR images", A. Bischoff-Grethe et al., Hum Brain Mapp., 28(9):892-903, 2007.
2. "Preventing facial recognition when rendering MR images of the head in three dimensions", F. Budin et al., Med Image Anal., 12(3):229-239, 2008.
3. "Obscuring Surface Anatomy in Volumetric Imaging Data", M. Milchenko et al., Neuroinformatics, 11(1):65-75, 2013.
4. "Implications of Surface-Rendered Facial CT Images in Patient Privacy", J. J. Chen et al., AJR Am J Roentgenol. 202(6):1267-1271, 2014.
5. "Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications", C. L. Parks et al., J Digit Imaging., 30(2):204-214, 2017.
6. "Biometric correspondence between reface computerized facial approximations and CT-derived ground truth skin surface models objectively examined using an automated facial recognition system", C. L. Parks et al., Forensic Sci Int., 286:8-11, 2018.
7. "Refacing: reconstructing anonymized facial features using GANs", Abramian et al., IEEE International Symposium on Biomedical Imaging (ISBI), 1104-1108, 2019.
8. "Identification of Anonymous MRI Research Participants with Face-Recognition Software", C. G. Schwarz et al., N Engl J Med., 381(17):1684-1686, 2019.
9. "Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives", C. G. Schwarz et al., NeuroImage, 231:117845, 2021.
10. "Effects of Defacing Whole Head MRI on Neuroanalysis", C. Gao et al., SPIE Medical Imaging: Image Processing, 2022.
11. "Face recognition from research brain PET: An unexpected PET problem", C. G. Schwarz et al., NeuroImage, 258:119357, 2022.

Manuscripts marked in green identify the issue and describe potential solutions (some more aggressive than others, i.e Bischoff-Grethe et al. vs. Milchenko et al.).

Bottom line, if you work with patient data be respectful of their privacy. Yes you are legally required to comply with privacy laws, but sometimes people forget - to err is human....

See this ppt presentation for a detailed discussion of data sharing, medical images as a case study.

Survey Papers and Books

Papers are listed according to subject, and are in reverse chronological order. Most, if not all, of these are cited in this CAIMR technical report.

1. Books

   • F. A. Jolesz ed., Intraoperative Imaging and Image-Guided Therapy, Springer, 2014.
   • T. Peters, K. Cleary eds., Image-Guided Interventions: Technology and Applications, Springer, 2008.
   • R. H. Taylor, S. Lavallee, G. S. Burdea, R. Mosges eds., Computer-Integrated Surgery: Technology and Clinical Applications, MIT Press, 1995.

2. Medical robotics

   • V. Vitiello, S.-L. Lee, T. P. Cundy, G.-Z. Yang, "Emerging robotic platforms for minimally invasive surgery", IEEE Rev Biomed Eng., 6, 111-126, 2013.
   • G. Dogangil, B. L. Davies, F. Rodriguez y Baena, "A review of medical robotics for minimally invasive soft tissue surgery", Proc Inst Mech Eng H, 224(5), 653-679, 2010.
   • G. D. Hager, A. M. Okamura, P. Kazanzides, L. L. Whitcomb, G. Fichtinger and R. H. Taylor, "Surgical and Interventional Robotics: Part III Surgical Assistance Systems", IEEE Robotics and Automation, 15(4), 84-93, 2008.
   • G. Fichtinger, P. Kazanzides, A. M. Okamura, G. D. Hager L. L. Whitcomb and Russell H. Taylor, "Surgical and Interventional Robotics: Part II Surgical CAD-CAM Systems", IEEE Robotics and Automation, 15(3), 94-102, 2008.
   • P. Kazanzides, G. Fichtinger, G. D. Hager, A. M. Okamura, L. L. Whitcomb and R. H. Taylor, "Surgical and Interventional Robotics - Core Concepts, Technology, and Design", IEEE Robotics and Automation, 15(2), 122-130, 2008.
   • R. H. Taylor and D. Stoianovici, "Medical Robotics in Computer-Integrated Surgery", IEEE Transactions on Robotics and Automation, 19(5), 765-781, 2003.
   • K. Cleary, C. Nguyen, "State of the art in surgical robotics: clinical applications and technology challenges", Computer Assisted Surgery, 6(6), 312-328, 2001.
   • R. D. Howe, Y. Matsuoka, "Robotics for Surgery", Annual Review of Biomedical Engineering, 1, 211-240, 1999.

3. Medical image computing

   • J. S. Duncan, N. Ayache, "Medical Image Analysis: Progress over Two Decades and Challenges Ahead", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 85-105, 2000.

4. Computer assisted interventions

   • K. Cleary, T. Peters, "Image-guided interventions: technology review and clinical applications", Annu. Rev. Biomed. Eng., 12, 119-142, 2010.
   • T. M. Peters, "Image-guidance for surgical procedures", Physics in Medicine and Biology, 51(14), R505–R540, 2006.
   • R. L. Galloway Jr., "The Process and Development of Image-Guided Procedures", Annual Review of Biomedical Engineering, 3, 83-108, 2001.
   • T. M. Peters, "Image-guided surgery: From X-rays to Virtual Reality", Computer Methods in Biomechanics and Biomedical Engineering, 4(1), 27-57, 2000.

5. Computer assisted orthopaedic surgery (CAOS)

   • F. Langlotz, L. P. Nolte, "Technical Approaches to Computer-Assisted Orthopedic Surgery", European Journal of Trauma, 30(1), 1-11, 2004.
   • F. Langlotz, L. P. Nolte, "Computer-Assisted Orthopaedic Surgery From Theory to the Operating Room", Techniques in Orthopaedics, 18(2), 140-148, 2003.

6. Medical imaging and image processing

   • A. B. Wolbarst, W. R. Hendee, "Evolving and Experimental Technologies in Medical Imaging", Radiology, 238(1), 16-39, 2006.
   • F. A. Jolesz, "Future perspectives for intraoperative MRI", Neurosurgical Clinics of North America, 16(1), 201-213, 2005.

7. Data visualization

   • H. Pfister et al., "The transfer function bakeoff", IEEE Comput. Graph. Appl., 21(3), 16-22, 2001.

8. Segmentation

   • T. Heimann, H-P Meinzer, "Statistical shape models for 3D medical image segmentation: A review", Med. Imag. Anal., 13(4), 543-563, 2009.
   • J. S. Suri, K. Liu, L. Reden, S. Laxminarayan, "A review on MR vascular image processing algorithms: acquisition and prefiltering: part I", IEEE Trans. Inform. Technol. Biomed., 6(4), 324-337, 2002.
   • J. S. Suri, K. Liu, L. Reden, S. Laxminarayan, "A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II", IEEE Trans. Inform. Technol. Biomed., 6(4), 338-350, 2002.
   • J. S. Suri, K. Liu, S. Singh, S. Laxminarayan, X. Zeng, L. Reden, "Shape recovery algorithms using level sets in {2-D/3-D} medical imagery: a state-of-the-art review", IEEE Trans. Inform. Technol. Biomed., 6(1), 8-28, 2002.
   • J. S. Suri, S. Singh, L. Reden, "Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part I): A State-of-the-Art Review", Pattern Anal. Appl., 5(1), 46-76, 2002.
   • J. S. Suri, S. Singh, L. Reden, "Fusion of Region and Boundary/Surface-Based Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part-II): A State-of-the-Art Review", Pattern Anal. Appl., 5(1), 77-98, 2002.
   • D. L. Pham, C. Xu, J. L. Prince, "Current Methods in Medical Image Segmentation", Annual Review of Biomedical Engineering, 2(1), 315-337, 2000.
   • T. McInerney, D. Terzopoulos, "Deformable Models in Medical Image Analysis: A Survey", Medical Image Analysis, 1(2), 91-108, 1996.

9. Registration

   • A. Sotiras, C. Davatzikos, N. Paragios, "Deformable Medical Image Registration: A Survey", IEEE Transactions on Medical Imaging, 32(7), 1153-1190, 2013.
   • M. Holden, "A Review of Geometric Transformations for Nonrigid Body Registration", IEEE Transactions on Medical Imaging, 27(1), 111-128, 2008.
   • B. Zitova, J. Flusser, "Image registration methods: a survey", Image and Vision Computing, 21(11), 977-1000, 2003.
   • J. P. W. Pluim, J. B. A. Maintz, M. A. Viergever, "Mutual-Information-Based Registration of Medical Images: A Survey", IEEE Transactions on Medical Imaging, 22(8), 986-1004, 2003.
   • D. L. G. Hill, P. G. Batchelor, M. Holden, D. J. Hawkes, "Medical image registration", Physics in Medicine and Biology, 46(3), R1-R45, 2001.
   • H. Lester, S. R. Arridge, "A survey of hierarchical non-linear medical image registration", Pattern Recognition, 32(1), 129-149, 1999.
   • J. B. A. Maintz, M. A. Viergever, "A survey of Medical Image Registration", Medical Image Analysis, 2(1), 1-37, 1998.

10. Tracking systems

    • G. Welch, E. Foxlin, "Motion Tracking: No Silver Bullet, but a Respectable Arsenal", IEEE Computer Graphics and Applications, 22(6), 24-38, 2002.
    • V. V. Kindratenko, "A survey of electromagnetic position tracker calibration techniques", Virtual Reality: Research, Development and Applications, 5(3), 169-182, 2000.

11. Human computer interaction (HCI)

    • S. D. Olabarriaga, A. W. M. Smeulders, "Interaction in the segmentation of medical images: A survey", Medical Image Analysis, 5(2), 127-142, 2001.

12. Interpolation

    • E. Meijering, "A Chronology of Interpolation: From Ancient Astronomy to Modern Signal and Image Processing", Proceedings of the IEEE, 90(3), 319-342, 2002.
    • T. M. Lehmann and C. Gonner and K. Spitzer, "Survey: Interpolation Methods in Medical Image Processing", IEEE Transactions on Medical Imaging, 18(11), 1049-1075, 1999.

13. Surgical Workflow / Context Aware Systems

    • F. Lalys and P. Jannin, "Surgical process modelling: a review", Int J Comput Assist Radiol Surg., 9(3), 495-511, 2014.

14. Augmented Reality

    • Z. Yaniv, C. A. Linte, "Applications of Augmented Reality in the Operating Room", ch. 19 in "Fundamentals of Wearable Computers and Augmented Reality", CRC Press, 2015.
    • M. Kersten-Oertel, P. Jannin and D. L. Collins, "Augmented Reality for Image-Guided Surgery", ch. 20 in "Fundamentals of Wearable Computers and Augmented Reality", CRC Press, 2015.
    • M. Kersten-Oertel, P. Jannin and D. L. Collins, "The state of the art of visualization in mixed reality image guided surgery", Comput Med Imaging Graph., 37(2), 98-112, 2013.
    • M. Kersten-Oertel, P. Jannin and D. L. Collins, "DVV: a taxonomy for mixed reality visualization in image guided surgery", IEEE Trans Vis Comput Graph., 18(2), 332-352, 2012.

Journals and Conferences

Journals

1. IEEExplore
2. PubMed, National Library of Medicine publications data base. Many papers describing research funded by the NIH are freely available through this database even if they are not freely accessible through the publisher's site.
3. Medical Image Analysis
4. IEEE Transactions on Medical Imaging
5. IEEE Transactions on Biomedical Engineering
6. IEEE Journal of Biomedical and Health Informatics, formerly IEEE Transactions on Information Technology in Biomedicine (TITB)
7. Computer Aided Surgery (Wiley), moved to Computer Aided Surgery (Informa Healthcare)
8. Computerized Medical Imaging and Graphics
9. Medical Physics
10. Physics in Medicine and Biology
11. The International Journal of Medical Robotics and Computer Assisted Surgery
12. International journal of biomedical imaging, an open access journal, reversing the traditional publication model - access is free, authors pay (in this case per-page fee starts from the seventh page)
13. International Journal of Computer Assisted Radiology and Surgery.
14. Healthcare Technology Letters.
15. Computer Methods and Programs in Biomedicine.
16. Medical and Biological Engineering and Computing (Springer).
17. Pattern Recognition (Elsevier)
18. Pattern Recognition Letters (Elsevier)
19. IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Computer Society)
20. CVGIP: Image Understanding (Elsevier)
21. CVGIP: Graphical Models and Image Processing (Elsevier)
22. Image and Vision Computing (Elsevier)
23. Computer Vision and Image Understanding (Elsevier)
24. International Journal of Computer Vision (Springer)
25. Ethics in Biology, Engineering and Medicine

Conferences

1. International Society and Conference Series on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
2. The international society for optical engineering (SPIE): medical imaging.
3. IEEE International Symposium on Biomedical Imaging (ISBI).
4. Information Processing in Medical Imaging (IPMI).
5. Information Processing in Computer-Assisted Interventions (IPCAI).
6. IEEE Engineering in Medicine and Biology Conference (EMBC) the annual IEEE Engineering in Medicine and Biology Society (EMBS) conference.
7. Computer Assisted Radiology and Surgery (CARS).
8. Computer Assisted Orthopaedic Surgery (CAOS) conference, the annual International Society for Computer Assisted Orthopaedic Surgery (CAOS-international) conference.
9. Medicine meets virtual reality (MMVR).

Who, Where - Researchers and Laboratories

• Bioengineering Initiative at the Sheikh Zayed Institute for Pediatric Surgical Innovation, (Kevin Cleary, Marius Linguraru, Raj Shekhar).
• Computer Assisted Surgery and Medical Image Processing Laboratory,Hebrew University (Leo Joskowicz).
• Bern ARTORG Center for Biomedical Engineering Research (Stefan Weber, Raphael Sznitman...) and Institute for Surgical Technology and Biomechanics (Lutz-Peter Nolte, Guoyan Zheng, ...).
• Biomedical Image Analysis Laboratory Oxford (Alison Noble, Vicente Grau...)
• Epione Research Project, INRIA Sophia Antipolis (Nicholas Ayache, Herve Delingette,...).
• Division of Informatics, Imaging & Data Sciences (Manchester University) Active Appearance and Active Shape Models (Tim Cootes,...).
• Image Sciences Institute Utrecht University (Josien Pluim,...).
• Johns Hopkins Group (Russ Taylor, Greg Hager, Peter Kazanzides...).
• Centre for Medical Image Computing, University College London (Dave Hawkes, ...).
• Medical Robotics Technology Center (MRTC) (Cam Riviere, Howie Choset, John Galeotti...).
• Mechatronics in Medicine Laboratory (Ferdinando Rodriguez y Baena, Brian Davies).
• Yale Image Processing and Analysis Group (James Duncan, Lawrence Staib,...).
• Boston groups: MIT AI Lab, Medical Vision Group and Brigham and Women's Hospital Surgical Planning Laboratory (SPL) (Eric Grimoson, Polina Goland, William Wells III, Ron Kikinis, Carl-Fredrik Westin (Laboratory of Mathematics in Imaging), Noby Hata (Surgical Navigation and Robotics Laboratory) , Simon Warfield (Computational Radiology Laboratory) ...
• Queens university groups Med-i Lab, Medical Computing Lab (MCL) and Laboratory for Percutaneous Surgery; Perk-Lab. (Parvin Mousavi, Randy Ellis, Gabor Fichtinger...).
• UBC Robotics and Control Laboratory (Purang Abolmaesumi, Rob Rohling, Tim Salcudean).
• UNC groups Medical Image Display and Analysis Group (MIDAG), Neuro Image Research and Analysis Laboratories (NIRAL), Image Display, Enhancement, and Analysis (IDEA) (Stephen Pizer, Martin Styner, Dinggang Shen...).
• Biomedical Imaging Group Erasmus MC - University Medical Center Rotterdam (Wiro Niessen, Erik Meijering,...).
• Laboratory of Imaging Technologies University of Ljubljana, Ljubljana, Slovenia (Franjo Pernus, Bostjan Likar...).
• MediCIS Research Team, University of Rennes, and their site on validation (Pierre Jannin,...).
• Digital Image Processing, Center of Biomedical Engineering and Physics, Medical University of Vienna (Wolfgang Birkfellner, Michael Figl,...).
• London groups Imperial College Biomedical Image Analysis group, Imperial College's Hamlyn Centre for robotics research UCL's Surgical Robot Vision, UCL's Centre for Medical Image Computing (CMIC), KCL's Centre for Medical Engineering (Daniel Rueckert, Ben Glocker, Guang-Zhong Yang, Dan Stoyanov, Dave Hawkes, Seb Ourselin, Tom Vercauteren, Julia Schnabel, ...).
• Processing Speech and Images, K. U. Leuven (Frederik Maes, Paul Suetens, Dirk Vandermeulen,...).
• Vanderbilt Institute for Surgery and Engineering, (Mike Miga, Benoit Dawant, Bennett Landman, Nabil Simaan, Bob Webster...).
• U Penn groups, Medical Image Processing Group (MIPG) (Jayaram Udupa,...), Penn Image Computing and Science Lab (PICSL) (Jim Gee, Paul Yushkevich,...), Section for Biomedical Image Analysis (Christos Davatzikos, Ragini Verma...).
• Biomedical Computation Group Dartmouth College (Keith Paulsen, Alex Hartov,...).
• Chair for Computer Aided Medical Procedures and Augmented Reality Technical University Munich (Nassir Navab, Gudrun Klinker).
• Scientific Computing and Imaging (SCI) Institute University of Utah (Sarang Joshi, Ross Whitaker,...).
• Biomedical Imaging Resource Mayo clinic (David Holmes,...).
• The multidimensional image processing lab, Penn State (Bill Higgins).
• Computer Vision Laboratory, ETH Zurich, Department of Information Technology and Electrical Engineering (Gábor Székely).
• Ultrasound and image-guided therapy, SINTEF Norway (Thomas Lango, Frank Lindseth...).
• 4D Visualization Laboratory, Innsbruck Medical University, Austria (Wolfgang Freysinger).
• Humanoids and Intelligence Systems Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology (Rudiger Dillmann, Stefanie Speidel...)
• Computer-Assisted Medical Interventions, German Cancer Research Center (Lena Maier-Hein...)
• Biomedical Modeling, Visualization and Image-guided Navigation Lab, Rochester Institute of Technology (Cristian Linte...)

Resources

All of the toolkits, tools and data are free, links to commercial companies selling phantoms are obviously not. Most of the software is also open source. This does not mean that it is fun downloading and installing it.

Software

• National Institute of Standards and Technology (NIST)
• Decision tree for optimization
• Netlib (mathematical software and papers)
• NLopt, free open source non linear optimization library, callable from C++, Matlab, python...
• Ceres Solver, free open source non linear least squares optimization library, C++, from google and used by google, new BSD license. Enables the use of analytic derivatives, auto-differentiation, or numeric differentiation. I have a soft spot in my heart for auto differentiation as it was my first encounter with research.
• COmputational INfrastructure for Operations Research (COIN-OR), linear, mixed-integer linear, and nonlinear programming sources.
• GNU Linear Programming Kit (GLPK), linear programming sources.
• ALGLIB, numerical analysis library, both free for open source and commercial licenses available.
• Optimized software from Intel
  • Integrated Performance Primitives (IPP), signal and image processing.
  • Math Kernel Library (MKL), linear algebra (BLAS,LAPACK) and DFT.
  • OpenCV, vision and image processing related algorithms. The actual download is from sourceforge.
• MATLAB Central, user contributed MATLAB code.
• VXL Vision Something Library, vision related code (image manipulation, matrix decompositions, optimization etc.)
• Eigen a templated linear algebra library.
• Registration and segmentation toolkit ITK. Multi platform and compiler. Written in C++, highly templated. Supports data of arbitrary dimension (not limited to 2D or 3D). Programming language support for java, tcl/tk and python, but most people end up working with C++. Pipeline approach with data sources and sinks (the results of your algorithms).
• MATLAB-ITK interface MATITK. For those not versed in C++. Provides access to ITK algorithms through MATLAB.
• Visualization toolkit, VTK.
• Elastix, rigid and nonrigid registration (uses ITK).
• Statismo, open source framework for statistical shape modeling (uses ITK), source on github.
• RTK, the reconstruction toolkit, circular cone-beam CT reconstruction (uses ITK).
• Scene graph based 3D visualization toolkit providing surface and volume rendering capabilities, Coin and SIM Voleon. Kind of a free version of Open Inventor.
• Compiling ITK and VTK, CMake.
• Image guided surgery toolkit IGSTK. Toolkit for developing image-guided medical applications. Provides a component-based framework for development based on the explicit use of state machines. Built upon ITK and VTK with additional components such as viewer components for standard medical views (axial sagittal coronal) and support for a variety of tracking systems.
• cisst package. A set of libraries for developing computer assisted intervention systems. Provides error handling, logging and efficient linear algebra numerical routines.
• GUI toolkits:
  • FLTK, multi platform, not too many widgets.
  • Qt, a commercial product that has a freely available version for open software (LGPL) developers. Note that on windows platform the binary releases are using the minGW compiler. If using msvc it is best to compile from source (takes a lot of time).
• MITK, Medical Imaging Interaction Toolkit. Interaction toolkit built on top of ITK and VTK providing single data multiple views interaction, undo actions and more.
• CamiTK, Computer Assisted Medical Intervention Tool Kit. Toolkit built on top of ITK, VTK, Qt, SOFA etc. for computer assisted interventions and modeling (FEM).
• CustusX,Navigation system for Image-Guided Intervention, open source from SINTEF. Focus is on integration of ultrasound.
• IbisNeuronav,Navigation system for Image-Guided Neurosurgery, open source from the Montreal Neurological Institute. Focus is on augmented reality.
• scikit surgery CAI in Python from UCL, and complete course material using the toolkit.
• Image analysis from the NIH ImageJ. Written in Java, so it is implicitly multi platform. Plugin type of architecture, and macro scripting language. Extensible through the plugin architecture either in java or in your language of choice (C++) and java using the java native interface (JNI) as the interface between your native code and java (this approach may loose the multi platform benefits of java).
• Another analysis program from the NIH, Medical Image Processing, Analysis, and Visualization (MIPAV). Java program, so implicit multi platform. No way to extend.
• OsiriX Image processing and visualization software. Only for the Mac. Can visualize time series (4D data).
• AMIDE Medical Imaging Data Examiner. Can visualize multiple volumetric data sets in conjunction (data fusion).
• Clear Canvas an open source PACS workstation. A commercial FDA approved version is also available.
• Slicer Environment for visualization, registration, segmentation, and quantification of medical data.
• NITRC, neuroimaging resources, data and software for analyzing brain images.
• medInria, open source program for image analysis (visualization, registration, segmentation, DTI), similar in many ways to 3D Slicer.
• Tracking related toolkits:
  • ARToolkit - library for building augmented reality systems with (web) camera based tracking.
  • OpenTracker - A meta tracker toolkit. An abstraction layer that separates between actual hardware tracker and the data abstraction of a tracker. Enables you to combine multiple trackers so that they appear as a single tracker (the classical example is to combine electromagnetic and optical trackers). In addition, you can add filters (e.g. predictive Kalman Filtering) between the hardware reported data and the output to the user of the tracker.
• Optimize your code using an open source multi-platform profiler, tau.
• Organize your group's work using free resources for open source projects, a wiki+source code repository+ticketing system:
  1. Assembla: Hosted service, wiki, ticketing, svn or git repository.
  2. Atlassian tools: ticketing (Jira+GreenHopper) and wiki (Confluence) from Atlassian, git as a repository.
• Some machine learning resources, just for good measure:
  • Support Vector Machine library (C/C++, modified BSD), libSVM and on github.
  • Weka library (Java, GPL).
  • A laundry list of deep learning software (Theano, Torch, Caffe, TensorFlow...).
  • Site listing open source machine learning software.
• Open Microscopy Environment, Remote Objects OMERO, manage and analyze microscopy images using client server architecture.

Data

• A portal site for a large number of biomedical image analysis challenges that make data available. The challenge with these challenges (pun intended) is that they often have a limited shelf life. That is, either the data is limited for use in the challenge, or you may find it hard to track down the postdoc who was in charge of providing the password to download the data. On the other hand you now know which group had the data and you can contact the head of the group who will point you to the person that can actually provide it to you.
• Vegetables MRI, scans of various vegetables. Not sure why these were acquired, but they are cool.
• Retrospective Image Registration Evaluation. Evaluate your 3D/3D rigid registration algorithm. Data includes CT/MR and PET/MR with known gold standard rigid registrations from implanted fiducials. Hosted by Kitware Inc., principle investigator J. M. Fitzpatrick, vanderbilt university.
• Reference data for evaluating 2D/3D (x-ray/MR/CT/CBCT) registration:
  1. Gold standard for 2D-3D registration, vertebra phantom and clinical cerebral angiograms. Brought to you by the Laboratory of Imaging Technologies in Ljubljana.
• National Biomedical Imaging Archive (NBIA), at the NIH.
• The Cancer Imaging Archive (TCIA). A large number of cancer related datasets (CT,MR, genomic...) of various anatomical sites (chest, prostate, head-neck...).
• MIAS, the Mammographic Image Analysis Society, digital mammography database, partially free.
• The visible human project:
  • All the raw data from the NIH, requires signing a license.
  • Some of the data translated to DICOM, no licensing involved.
• The virtual population, ten manually segmented MRI data from male and female adults and children. Not free and can only be used for research purposes.
• IBSR, Internet Brain Segmentation Repository, database of clinical MRI brain data sets and their semi-automatic expert segmentations. Useful as ground truth for evaluating segmentation algorithms.
• BrainWeb, from Montréal Neurological Institute, McGill University. An online interface for generating simulated brain MRI data sets. Useful as ground truth for evaluating segmentation algorithms.
• The Cardiac Atlas Project, cardiac data sets, some for unrestricted use some with.
• Brain Images of Tumors for Evaluation (BITE) database. Pre and post operative MR, and intraoperative ultrasound images from 14 brain tumor patients.
• Open-CAS database. Collection of datasets for validating computer-assisted surgery (CAS).
• Osirix sample data. Various CT and MR data, primarily useful for visualization.
• Open Access Series of Imaging Studies (OASIS). MRI brain scans.
• Give A Scan. Lung CT images and clinical information provided by patients [I really appreciate this initiative as it allows researchers that do not have access to large datasets to develop algorithms that truly work, and not just on their 10 scans].
• SpineWeb, all things spine, data, segmentation, registration.
• POPI model, point-validated pixel-based breathing thorax model. A set of 4D CTs of the thorax with multiple corresponding landmarks identified across the CTs.
• Public Domain Database for Computational Anatomy, PDDCA, 33 patient CTs of the head with manual segmentations of the left and right parotid glands, brainstem, optic chiasm, left and right optic nerves, mandible, left and right submandibular glands.

Phantoms and hardware

• CIRS tissue simulation and phantom technology. Offer a variety of anthropomorphic phantoms. I personally have only worked with their interventional 3D abdominal phantom (can be imaged with CT, MR, and US).

  A word of caution on traveling with abdominal phantoms model 057, and 071 in your carry on luggage. From personal experience and that of others, it seems that these phantoms can trigger the chemical detection systems found at airports in the USA. Add the electronic equipment you are carrying (cables, laptop, tracking device) to the mix and you most likely will be delayed and questioned. While the TSA personal are polite this is not a recommended experience.

• Sawbones worldwide, various bone models.
• Limbs and Things, medical simulation models. We have used their "skin" to cover an anthropomorphic phantom.
• Gaumard, simulators (anthropomorphic phantoms) for health care education.
• Kyoto Kagaku, anthropomorphic phantoms for health care education and imaging.
• Shelley Medical Imaging Technologies, blood flow and vascular phantoms.
• McMaster-Carr, hardware for building phantoms for calibration, etc.
• Smooth-On, foam and plastics for molding phantoms.
• Beekley corporation, skin adhesive fiducials visible in MR/CT.
• IZI Medical, skin adhesive fiducials visible in MR/CT.
• Northern digital Inc. (NDI), optical (IR) and electromagnetic trackers.
• ClaroNav, formerly Claron Technology Inc., optical (standard video) trackers.
• Ascension Technology Corp. (now owned by NDI), electromagnetic trackers.
• Atracsys LLC, optical (IR) trackers.
• Axios 3D Services, optical (IR) trackers.

Publishing

• LaTex for your windows typewriter, MikTex, or for your Mac, not surprisingly called MacTex.
• Use vector graphics and latex to create figures that scale gracefully, either with Xfig (use cygwin version on windows), or inkscape for general figures and Dia for diagrams such as UML.
• Integrated development environments for LaTex on your windows typewriter, TeXnicCenter, all platforms, TeXstudio, or a really nice shareware program for windows, WinEdt. Also, overleaf is an on-line service if you don't want to install anything (for a fee you can use the platform for collaborative work).
• Managing your bibliography in bibtex JabRef (requires java).
• If you really must use Microsoft Word:
  • Manage your bibliography using Zotero.
  • Print to pdf using CutePDF.
• If your collaborators only work with Word:
  • Use latex2rtf to convert your latex to rich text format which is readable by Word (latex2rtf -o output.docx -b bbl_file input.tex)
  • In Word2013 on windows (didn't work on mac) open the pdf generated from your LaTex. Word will convert it automatically. Formulas become pictures so for the final version generate the pdf from the LaTex.

Conference Organization

• EasyChair (help desk support is not free).
• CMT from Microsoft research.