Review of the Software Used for 3D Volumetric Reconstruction of the Liver
In medical imaging, segmentation of different areas of human body like bones, organs, tissues, etc. is an important issue. Image segmentation allows isolating the object of interest for further processing that can lead for example to 3D model reconstruction of whole organs. Difficulty of this procedure varies from trivial for bones to quite difficult for organs like liver. The liver is being considered as one of the most difficult human body organ to segment. It is mainly for its complexity, shape versatility and proximity of other organs and tissues. Due to this facts usually substantial user effort has to be applied to obtain satisfactory results of the image segmentation. Process of image segmentation then deteriorates from automatic or semi-automatic to fairly manual one. In this paper, overview of selected available software applications that can handle semi-automatic image segmentation with further 3D volume reconstruction of human liver is presented. The applications are being evaluated based on the segmentation results of several consecutive DICOM images covering the abdominal area of the human body.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099182Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3667
 Suhuai, L., Xuechen, L., Jiaming, L., 2014. Review on the methods of automatic liver segmentation from abdominal images. In Journal of Computer and Communications, Vol. 2, No. 2, pp 1-7. Scientific Research. DOI: 10.4236/jcc.2014.22001.
 Mharib, A., Ramli, A., Mashohor, S., Mahmood, R., 2012. Survey on liver CT image segmentation methods. In Artificial Intelligence Review, Vol. 37, No. 2 , pp 83-95. Springer Netherlands. DOI: 10.1007/s10462- 011-9220-3.
 ITK - Segmentation & Registration Toolkit, 2014. Available from:
 VTK - The Visualization Toolkit, 2014. Available from:
 ITK-SNAP Home Page, 2014. Available from:
 General Public License, 2007. Available from:
 Yushkevich, P., Piven, J., Cody, H., Smith, R., Ho, S., Gee, J., Gerig, G., 2006. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. In Neuroimage,Vol. 31, No. 3, pp. 1116-28. Elsevier. DOI: 10.1016/j.neuroimage.2006.01.015.
 DICOM Homepage, 2014. Available from:
 GeoS - Microsoft Research, 2014. Available from:
 Microsoft Research License Agreement, 2013. Available from:
 Criminisi, A., Sharp, T., Blake, A., 2008. GeoS: Geodesic Image Segmentation. In ECCV 2008, eds D. Forsyth, P. Torr, and A. Zisserman, Part I, LNCS 5302, pp. 99–112, Springer-Verlag Berlin Heidelberg.
 Jirik, M., Ryba, T., Svobodova, M., Mira, H., Liska, V., 2014. Lisa – Liver surgery analyser software development. In Proceedings of WCCM XI-ECCM V-ECFD VI. Barcelona.
 Lisa GitHub, 2012. Available from:
 Boykov, Y., Funka-Lea, G., 2006. Graph cuts and efficient N-D image segmentation. In International Journal of Computer Vision, Vol. 70, No. 2, pp 109–131. Kluwer Academic Publishers. DOI: 10.1007/s11263- 006-7934-5.
 Introduction – SlicerWeb, 2014. Available from:
 LicenseText – SlicerWeb, 2005. Available from:
 Ghosh, P., Antani, S. K., Long, L. R., Thoma, G. R., 2011. Unsupervised grow-cut: Cellular automata-based medical image segmentation. In Healthcare Informatics, Imaging and Systems Biology, pp 40-47. IEEE. DOI: 10.1109/HISB.2011.44.
 About OsiriX, 2014. Available from:
 License - license.pdf, 2007. Available from:
 DICOM files, 2014. Available from: