Assessment of DICOM Viewers Capable of Loading Patient-specific 3D Models Obtained by Different Segmentation Platforms in the Operating Room

Patient-specific 3D models obtained by the segmentation of volumetric diagnostic images play an increasingly important role in surgical planning. Surgeons use the virtual models reconstructed through segmentation to plan challenging surgeries. Many solutions exist for the different anatomical districts and surgical interventions. The possibility to bring the 3D virtual reconstructions with native radiological images in the operating room is essential for fostering the use of intraoperative planning. To the best of our knowledge, current DICOM viewers are not able to simultaneously connect to the picture archiving and communication system (PACS) and import 3D models generated by external platforms to allow a straight integration in the operating room. A total of 26 DICOM viewers were evaluated: 22 open source and four commercial. Two DICOM viewers can connect to PACS and import segmentations achieved by other applications: Synapse 3D® by Fujifilm and OsiriX by University of Geneva. We developed a software network that converts diffuse visual tool kit (VTK) format 3D model segmentations, obtained by any software platform, to a DICOM format that can be displayed using OsiriX or Synapse 3D. Both OsiriX and Synapse 3D were suitable for our purposes and had comparable performance. Although Synapse 3D loads native images and segmentations faster, the main benefits of OsiriX are its user-friendly loading of elaborated images and it being both free of charge and open source.

[1]  Lubomir M. Hadjiiski,et al.  Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation. , 2007, Medical physics.

[2]  Jayaram K. Udupa,et al.  Surface and volume rendering in three-dimensional imaging: A comparison , 1991, Journal of Digital Imaging.

[3]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[4]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[5]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[6]  Davide Caramella,et al.  An integrated platform for an effective liver surgical planning through segmentation of multiphase CT datasets , 2013 .

[7]  R Janka,et al.  [Automated detection and volumetric segmentation of the spleen in CT scans]. , 2012, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[8]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[9]  M W Vannier,et al.  Three-dimensional hippocampal MR morphometry with high-dimensional transformation of a neuroanatomic atlas. , 1997, Radiology.

[10]  Akinobu Shimizu,et al.  Proposal of computer-aided detection system for three dimensional CT images of liver cancer , 2005 .

[11]  Cañas S Vides,et al.  Plugin for OsiriX: mean shift segmentation. , 2007, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[12]  Tracy L. Faber,et al.  A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution , 2014, IEEE Transactions on Image Processing.

[13]  Heang-Ping Chan,et al.  CT urography: segmentation of urinary bladder using CLASS with local contour refinement. , 2014, Physics in medicine and biology.

[14]  Mauro Ferrari,et al.  Value of multidetector computed tomography image segmentation for preoperative planning in general surgery , 2011, Surgical Endoscopy.

[15]  Mauro Ferrari,et al.  Segmentation procedure for the generation of a 3D model and solid replica of a human skull , 2012 .

[16]  Luca Antiga,et al.  Software for hepatic vessel classification: feasibility study for virtual surgery , 2009, International Journal of Computer Assisted Radiology and Surgery.

[17]  J. Hureau,et al.  The caudate lobe of the liver , 2005, Surgical and Radiologic Anatomy.

[18]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  S. Resnick,et al.  An image-processing system for qualitative and quantitative volumetric analysis of brain images. , 1998, Journal of computer assisted tomography.

[20]  Pablo Lamata,et al.  Use of the Resection Map system as guidance during hepatectomy , 2010, Surgical Endoscopy.