MESA: Complete approach for design and evaluation of segmentation methods using real and simulated tomographic images

Abstract In this paper we present MESA: a platform for design and evaluation of medical image segmentation methods. The platform offers a complete approach for the method creation and validation using simulated and real tomographic images. The system consists of several modules that provide a comprehensive workflow for generation of test data, segmentation method development as well as experiment planning and execution. The test data can be created as a virtual scene that provides an ideal reference segmentation and is also used to simulate the input images by a virtual magnetic resonance imaging (MRI) scanner. Both ideal reference segmentation and simulated images could be utilized during the evaluation of the segmentation methods. The platform offers various experimental capabilities to measure and compare the performance of the methods on various data sets, parameters and initializations. The segmentation framework, currently based on deformable models, uses a template solution for dynamical composition and creation of two- and three-dimensional methods. The platform is based on a client–server architecture, with computational and data storage modules deployed on the server and with browser-based client applications. We demonstrate the platform capabilities during the design of segmentation methods with the use of simulated and actual tomographic images.

[1]  Carolyn Kaut,et al.  MRI in Practice , 1993 .

[2]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[3]  Gavriil Tsechpenakis,et al.  Deformable Model-Based Medical Image Segmentation , 2011 .

[4]  M. Bronskill,et al.  T1, T2 relaxation and magnetization transfer in tissue at 3T , 2005, Magnetic resonance in medicine.

[5]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[6]  W. S. Rasband,et al.  ImageJ: Image processing and analysis in Java , 2012 .

[7]  Sébastien Thon,et al.  A Low Cost Antialiased Space Filled Voxelization Of Polygonal Objects , 2004 .

[8]  Brian Cabral,et al.  Accelerated volume rendering and tomographic reconstruction using texture mapping hardware , 1994, VVS '94.

[9]  D CohenLaurent.  On active contour models and balloons , 1991 .

[10]  Michael H. F. Wilkinson,et al.  CPM: a deformable model for shape recovery and segmentation based on charged particles , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Marek Kretowski,et al.  Fast 3D Segmentation of Hepatic Images Combining Region and Boundary Criteria , 2012 .

[12]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[13]  Marek Kretowski,et al.  HIST - an application for segmentation of hepatic images , 2011 .

[14]  N. Rofsky,et al.  MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. , 2004, Radiology.

[15]  Marek Kretowski,et al.  A Distributed Approach for Development of Deformable Model-Based Segmentation Methods , 2013, IP&C.

[16]  Robert M. O'Bara,et al.  Geometrically deformed models: a method for extracting closed geometric models form volume data , 1991, SIGGRAPH.

[17]  Amir Akramin Shafie,et al.  MRI Reconstruction Using Discrete Fourier Transform: A tutorial , 2008 .

[18]  Steve R. Gunn,et al.  A Robust Snake Implementation; A Dual Active Contour , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  J. Mizrachi,et al.  MRI in Practice, 4th ed. , 2012 .

[20]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[21]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[22]  Hans-Peter Meinzer,et al.  Computerized planning of liver surgery - an overview , 2002, Comput. Graph..

[23]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[24]  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 .

[25]  W. W. Hansen,et al.  The Nuclear Induction Experiment , 1946 .

[26]  Elena Casiraghi,et al.  Liver segmentation from computed tomography scans: A survey and a new algorithm , 2009, Artif. Intell. Medicine.

[27]  J Bittoun,et al.  A computer algorithm for the simulation of any nuclear magnetic resonance (NMR) imaging method. , 1984, Magnetic resonance imaging.

[28]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[29]  Rhodes Ml,et al.  Extracting Oblique Planes from Serial CT Sections , 1980, Journal of computer assisted tomography.

[30]  Vadim Kuperman,et al.  Magnetic Resonance Imaging: Physical Principles and Applications , 2000 .

[31]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[32]  Johan Montagnat,et al.  A review of deformable surfaces: topology, geometry and deformation , 2001, Image Vis. Comput..

[33]  M. Kretowski,et al.  Virtual magnetic resonance imaging - parallel implementation in a cluster computing environment , 2009 .