Skip to main content
Log in

Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning

Journal of Digital Imaging Aims and scope Submit manuscript

Cite this article


Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and 3D Slicer, the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude, this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. Values estimated from the manual segmentations.




  1. Lo Presti G, Carbone M, Ciriaci D, Aramini D, Ferrari M, Ferrari V: Assessment of DICOM viewers capable of loading patient-specific 3D models obtained by different segmentation platforms in the operating room. J Digit Imaging 28(5):518–527, 2015

    Article  Google Scholar 

  2. Ferrari V, Carbone M, Cappelli C, Boni L, Melfi F, Ferrari M, Mosca F, Pietrabissa A: Value of multidetector computed tomography image segmentation for preoperative planning in general surgery. Surg Endosc 26(3):616–626, 2012

    Article  Google Scholar 

  3. Azagury DE, Dua MM, Barrese JC, Henderson JM, Buchs NC, Ris F, Cloyd JM, Martinie JB, Razzaque S, Nicolau S, Soler L, Marescaux J, Visser BC: Image-guided surgery. Curr Probl Surg 52(12):476–520, 2015

    Article  Google Scholar 

  4. Haak D, Page C-E, Deserno TM: A survey of DICOM viewer software to integrate clinical research and medical imaging. J Digit Imaging 29(2):206–215, 2016

    Article  Google Scholar 

  5. Valeri G, Mazza FA, Maggi S, Aramini D, La Riccia L, Mazzoni G, Giovagnoni A: Open source software in a practical approach for post processing of radiologic images. Radiol Med 120(3):309–323, 2015

    Article  Google Scholar 

  6. Liao W, Deserno TM, Spitzer K: Evaluation of free non-diagnostic DICOM software tools. Proc SPIE 6919:691903, 2008

    Article  Google Scholar 

  7. Dice LR: Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945

    Article  Google Scholar 

  8. Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG: Diffusion tensor MR imaging and fiber tractography: Theoretic underpinnings. Am J Neuroradiol 29(4):632–641, 2008

    Article  CAS  Google Scholar 

  9. Virzì A, Marret J-B, Muller CO, Berteloot L, Boddaert N, Sarnacki S, Bloch I (2017) A new method based on template registration and deformable models for pelvic bones semi-automatic segmentation in pediatric MRI. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 323–326. IEEE

  10. Barfield W: Fundamentals of wearable computers and augmented reality. CRC Press, 2015

  11. Rengier F, Mehndiratta A, von Tengg-Kobligk H, Zechmann CM, Unterhinninghofen R, Kauczor H-U, Giesel FL: 3D printing based on imaging data: review of medical applications. Int J Comput Assist Radiol Surg 5(4):335–341, Jul 2010

    Article  CAS  Google Scholar 

  12. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M et al.: 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341, 2012

    Article  Google Scholar 

  13. Rivière D, Geffroy D, Denghien I, Souedet N, Cointepas Y: Anatomist: A python framework for interactive 3D visualization of neuroimaging data. In: Python in neuroscience workshop, 2011

    Google Scholar 

  14. Geffroy D, Rivière D, Denghien I, Souedet N, Laguitton S, Cointepas Y: Brainvisa: A complete software platform for neuroimaging. In: Python in neuroscience workshop. Paris: Euroscipy, 2011

    Google Scholar 

  15. Fischl B: FreeSurfer. NeuroImage 62(2):774–781, 2012 20 Years of fMRI

    Article  Google Scholar 

  16. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM: FSL. NeuroImage 62(2):782–790, 2012 20 Years of fMRI

    Article  Google Scholar 

  17. Schneider CA, Rasband WS, Eliceiri KW: NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675, 2012

    Article  CAS  Google Scholar 

  18. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage 31(3):1116–1128, 2006

    Article  Google Scholar 

  19. Toussaint N, Souplet J-C, Fillard P et al.: MedINRIA: Medical image navigation and research tool by INRIA. In: MICCAI, volume 7, page 280, 2007

    Google Scholar 

  20. McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL: Medical image processing, analysis and visualization in clinical research. In: 14th IEEE Symposium on Computer-Based Medical Systems (CBMS), 2001, pp. 381–386

    Google Scholar 

  21. Rosset A, Spadola L, Ratib O: OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17(3):205{216, 2004

    Article  Google Scholar 

  22. CIBC: Seg3D: Volumetric image segmentation and visualization. Scientific Computing and Imaging Institute (SCI), 2016, Downloaded from:

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Alessio Virzì.

Ethics declarations

All patients or patient’s parents gave their informed consent according to ethical board committee requirements (N°IMIS2015-04).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



A Software Tool Description

In this appendix, a short description of all the software tools of Table 1 is provided.

3D Slicer

3D Slicer [12] is a free, multi-platform, and open-source software for image analysis and visualization written in C++, Python, and Qt. The origin of this software was a project between different laboratories of the Brigham and Women’s Hospital and the MIT in 1998. In the following years, several improvements of the software capabilities were achieved through the support of the National Institute of Health (NIH). The main interface of 3D Slicer appears as a typical radiology workstation, allowing for a large number of different visualization configurations to analyze 2D, 3D, and 4D images. The platform also offers a large set of processing tools for different imaging modalities and applications (including segmentation, registration, and quantification).


Anatomist [13] is the visualization software generally associated with the software platform BrainVISA [14]. BrainVISA is an open-source software written in Python, offering different tools dedicated to the neuroimaging research and mainly developed by the French Alternative Energies and Atomic Energy Commission (CEA). Although BrainVISA is devoted to brain MRI, Anatomist can be used to visualize and segment other types of image volumes.


AW-Server is the commercial visualization software developed by GE Healthcare. The workstation, more than just allowing for the visualization and annotation of the images, offers a large number of advanced post-processing applications for different imaging modalities and clinical applications.


Freesurfer [15] is an open-source software platform, written in C++, developed by the Martinos Center for Biomedical Imaging of Boston. The software is particularly devoted to the analysis and visualization of structural and functional neuroimaging data, offering several tools for the automated segmentation of anatomical MR images and the analysis of diffusion MR data. Despite the strong focus on brain MRI, Freesurfer can be used to visualize and analyze through generic tools various types of multi-dimensional medical images.


FSL (the FMRIB Software Library) [16] is an open-source software library, written in C++, mainly developed by the FMRIB Analysis Group of the University of Oxford. The software is strongly devoted to the analysis of functional, structural, and diffusion MRI brain imaging data. Similarly to Freesurfer, although FSL is strongly devoted to the brain MRI data, it offers a generic viewer (FSLView) that allows visualizing and manually segmenting 3D images.


ImageJ [17] is a Java-based, open-source platform for image processing, developed by the NIH and constantly updated since 1997. Thanks to the collaborative efforts of its developer community, ImageJ offers several functionalities for performing a wide variety of image processing tasks. However, even if ImageJ supports multi-dimensional data, it appears more focused on the processing of 2D images.


ITK-SNAP [18] is an open-source software application based on ITKFootnote 2 and VTKFootnote 3 C++ libraries. It was developed by the University of Pennsylvania and the University of Utah, first released in 2004 but under a constant updating process. The platform allows for navigation within the images similar to a radiology workstation, and it was specifically developed for segmentation tasks, not focusing on other kinds of processing (e.g., filtering, registration).


Mango (Multi-image Analysis GUI) is a free Java-based viewer for medical images developed by the Research Imaging Institute of the University of Texas Health Science Center at San Antonio. The software includes a GUI for the visualization of 3D images as well as functionalities for different tasks such as registration, filtering, and segmentation. It can be extended through dedicated plugins.


MedInria [19] is an open-source platform for medical image processing developed by Inria, the French National Institute for computer science and applied mathematics. This platform manages the visualization of multi-dimensional data, and it includes processing and analysis of diffusion MR images (e.g., to provide tractography). MedInria also offers basic segmentation, registration, and filtering tools based on the ITK library.


MIPAV [20], acronym for Medical Image Processing Analysis and Visualization, is a Java-based open-source software supported by the NIH. It manages multi-modal and 3D images, even if its main interface appears better suited for the processing and visualization of 2D images. MIPAV offers several functionalities for different tasks such as filtering, registration, and segmentation on both 2D and 3D images.


Myrian® is a commercial software for medical image processing and visualization developed by Intrasense. It supports multi-modal images and offers different functionalities for tasks such as segmentation, quantification, and registration. A non-commercial version, Myrian® Studio, is freely available for research purposes and can be extended through dedicated plugins.

Olea Sphere®

Olea Sphere® is a commercial processing platform for CT and MRI, developed by the company Olea Medical. The workstation includes a generic DICOM viewer and offers different packages developed for specific medical applications (e.g., breast, head and neck, prostate).


OsiriX [21] is one of the most widely used DICOM viewers in the medical community. The OsiriX project started in 2003 at UCLA, Los Angeles, and in 2010, the first commercial version of the software (OsiriX MD) was released. OsiriX Lite is the free version of the commercial software OsiriX MD, intended for research purposes and offering reduced computational performances, but it still includes the functionalities needed in our application domain. The platform appears as a typical radiology workstation, supporting multi-modal images and strongly devoted to the visualization tasks, even if it includes also post-processing tools such as registration and segmentation.


Seg3D [22] is an open-source software platform for image visualization and segmentation of 3D images developed by the NIH Center for Integrative Biomedical Computing at the University of Utah. The platform focuses on segmentation tasks, even if some other functionalities such as filtering using several methods from the ITK library are present.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Virzì, A., Muller, C.O., Marret, JB. et al. Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning. J Digit Imaging 33, 99–110 (2020).

Download citation

  • Published:

  • Issue Date:

  • DOI: