Expert knowledge-guided segmentation system for brain MRI

We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise.

[1]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[2]  Edoardo Ardizzoneab,et al.  A KNOWLEDGE BASED APPROACH TO INTELLIGENT DATA ANALYSIS OF MEDICAL IMAGES , 2001 .

[3]  Alain Pitiot,et al.  Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming , 2002, IEEE Transactions on Medical Imaging.

[4]  David J. Hawkes,et al.  Incorporating connected region labelling into automated image registration using mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[5]  Paul M. Thompson,et al.  Texture based MRI segmentation with a two-stage hybrid neural classifier , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[6]  D. Louis Collins,et al.  Use of Registration for Cohort Studies , 2001 .

[7]  A. Toga,et al.  Mapping morphology of the corpus callosum in schizophrenia. , 2000, Cerebral cortex.

[8]  Alain Pitiot.  Automated Sagmentation of Cerebral Structures Incorporating Explicit Knowledge , 2003 .

[9]  Dominique Hasboun,et al.  Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures , 1998, MICCAI.

[10]  M S Brown,et al.  Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

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

[13]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[14]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[15]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

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

[17]  James S. Duncan,et al.  Neighbor-Constrained Segmentation with 3D Deformable Models , 2003, IPMI.

[18]  Andre Obenaus,et al.  A Reliable Method for Measurement and Normalization of Pediatric Hippocampal Volumes , 2001, Pediatric Research.

[19]  Hongyi Li,et al.  Object recognition in brain CT-scans: knowledge-based fusion of data from multiple feature extractors , 1995, IEEE Trans. Medical Imaging.

[20]  S. Loncaric,et al.  A rule-based approach to stroke lesion analysis from CT brain images , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[21]  R. Woods,et al.  Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain , 2000, Human brain mapping.

[22]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[23]  Sébastien Ourselin,et al.  Reconstructing a 3D structure from serial histological sections , 2001, Image Vis. Comput..

[24]  Hervé Delingette,et al.  General Object Reconstruction Based on Simplex Meshes , 1999, International Journal of Computer Vision.

[25]  R. Rauch,et al.  Variability of corpus callosal area measurements from midsagittal MR images: effect of subject placement within the scanner. , 1996, AJNR. American journal of neuroradiology.

[26]  Stéphane Lavallée,et al.  Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery , 1999, Medical Image Anal..

[27]  Kevin T Foley,et al.  Intraoperative Spinal Navigation , 2003, Spine.

[28]  Bostjan Likar,et al.  Segmenting Articulated Structures by Hierarchical Statistical Modeling of Shape, Appearance, and Topology , 2001, MICCAI.

[29]  G. Borgefors.  Distance transformations in arbitrary dimensions , 1984 .

[30]  Nicholas Ayache,et al.  Iconic feature based nonrigid registration: the PASHA algorithm , 2003, Comput. Vis. Image Underst..

[31]  Hervé Delingette,et al.  Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis , 1999, IPMI.

[32]  Nicholas Ayache,et al.  Epidaure: A research project in medical image analysis, simulation, and robotics at INRIA , 2003, IEEE Transactions on Medical Imaging.

[33]  Vincent Barra,et al.  Automatic segmentation of subcortical brain structures in MR images using information fusion , 2001, IEEE Transactions on Medical Imaging.

[34]  Yali Amit,et al.  Graphical Templates for Model Registration , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Milan Sonka,et al.  Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples , 2000, IEEE Transactions on Medical Imaging.

[36]  Brian O'Sullivan,et al.  New TNM staging criteria for head and neck tumors. , 2003, Seminars in surgical oncology.

[37]  Johan Montagnat,et al.  Globally constrained deformable models for 3D object reconstruction , 1998, Signal Process..

[38]  Martin Styner,et al.  Statistical shape analysis of neuroanatomical structures based on medial models , 2003, Medical Image Anal..

[39]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[40]  James S. Duncan,et al.  Boundary Finding with Parametrically Deformable Models , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Ron Kikinis,et al.  Statistical Validation of Automated Probabilistic Segmentation against Composite Latent Expert Ground Truth in MR Imaging of Brain Tumors , 2002, MICCAI.

[42]  U. Grenander,et al.  Hippocampal morphometry in schizophrenia by high dimensional brain mapping. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Guido Gerig,et al.  Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation , 2001, MICCAI.

[44]  W. Eric L. Grimson,et al.  Coupled Multi-shape Model and Mutual Information for Medical Image Segmentation , 2003, IPMI.