A pipeline for interactive cortex segmentation

In various clinical or research scenarios, such as neurosurgical intervention planning, diagnostics, or clinical studies concerning neurological diseases, cortex segmentation can be of great value. As, e.g., the visualization of the cortical surface along with target and risk structures enables conservative access planning and gives context information about the patient-specific anatomy. We present an interactive cortex segmentation pipeline (CSP) for T1-weighted MR images, utilizing watershed and level set methods. It is designed to allow the user to adjust the intermediate results at any stage of the segmentation process. Particular attention is paid to the appropriate visualization of the segmentation in the context of the original data for verification and to different interaction methods (manual editing, parameter tuning, morphological operations). Evaluation of the interactive CSP is performed with the Segmentation Validation Engine (SVE) by Shattuck et al. (NeuroImage 45(2):431–439, 2009). The segmentation quality of our method is comparable to the best results of three different established methods: the brain extraction tool (BET), brain surface extractor (BSE), and hybrid watershed algorithm (HWA). Being designed for interaction, the CSP integrates the users’ expertise by allowing him to perform correction at any stage of the pipeline, enabling him to easily achieve a segmentation fulfilling his specific needs.

[1]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[2]  Kentaro Kotani,et al.  An analysis of muscular load and performance in using a pen-tablet system. , 2003, Journal of physiological anthropology and applied human science.

[3]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[4]  Gregory G. Brown,et al.  Quantitative evaluation of automated skull‐stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location , 2006, Human brain mapping.

[5]  J Sijbers,et al.  Watershed-based segmentation of 3D MR data for volume quantization. , 1997, Magnetic resonance imaging.

[6]  Dirk Bartz,et al.  A Cortex Segmentation Pipeline for Neurosurgical Intervention Planning , 2010, Bildverarbeitung für die Medizin.

[7]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[8]  Guang H. Yue,et al.  Automated Histogram-Based Brain Segmentation in T1-Weighted Three-Dimensional Magnetic Resonance Head Images , 2002, NeuroImage.

[9]  W. A. Hanson,et al.  Interactive 3D segmentation of MRI and CT volumes using morphological operations. , 1992, Journal of computer assisted tomography.

[10]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[11]  Arnold W. M. Smeulders,et al.  Setting the Mind for Intelligent Interactive Segmentation: Overview, Requirements, and Framework , 1997, IPMI.

[12]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[13]  Arthur W. Toga,et al.  Online resource for validation of brain segmentation methods , 2009, NeuroImage.

[14]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Heinz-Otto Peitgen,et al.  The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform , 2000, MICCAI.

[16]  Arthur W. Toga,et al.  Skull-stripping magnetic resonance brain images using a model-based level set , 2006, NeuroImage.

[17]  Lenore J. Launer,et al.  Analysis and validation of automated skull stripping tools: A validation study based on 296 MR images from the Honolulu Asia aging study , 2006, NeuroImage.

[18]  Chulhee Lee,et al.  Skull stripping based on region growing for magnetic resonance brain images , 2009, NeuroImage.

[19]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[20]  Chao Chen,et al.  Statistical morphological skull stripping of adult and infant MRI data , 2007, Comput. Biol. Medicine.

[21]  David A. Rottenberg,et al.  Quantitative comparison of four brain extraction algorithms , 2004, NeuroImage.

[22]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.