Image analysis for patient management in colorectal cancer

Abstract Colorectal cancer is a major health issue in the western world and accurate staging of this disease is crucial for informing patient management decisions, particularly for the surgically complex tumors. Current methods used by clinicians are primarily subjective and do not make use of much of the information potentially available from MR images. We are developing a series of image analysis methods to aid clinicians: by removing MR artifacts, segmenting and visualizing the data in three dimensions, and automatically detecting the lymph nodes and predicting whether or not they are involved in malignancy. This information is then displayed in a way that is useful to the clinicians as they assess the best course of treatment.

[1]  Andrew Blake,et al.  A framework for spatiotemporal control in the tracking of visual contours , 1993, International Journal of Computer Vision.

[2]  Michael Brady,et al.  A Non-parametric Model for Partial Volume Segmentation of MR Images , 2005, BMVC.

[3]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[4]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[5]  Michael Brady,et al.  Extracting and visualizing physiological parameters using dynamic contrast-enhanced magnetic resonance imaging of the breast , 2005, Medical Image Anal..

[6]  D. Koh,et al.  Rectal cancer: mesorectal lymph nodes at MR imaging with USPIO versus histopathologic findings--initial observations. , 2004, Radiology.

[7]  Michael Brady,et al.  Non-rigid Registration for Colorectal Cancer MR Images , 2005, CVBIA.

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

[9]  R. Newcombe,et al.  Morphologic predictors of lymph node status in rectal cancer with use of high-spatial-resolution MR imaging with histopathologic comparison. , 2003, Radiology.

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

[11]  Guido Gerig,et al.  Segmentation of 3D Objects from MRI Volume Data Using Constrained Elastic Deformations of Flexible Fourier Surface Models , 1995, CVRMed.

[12]  P. Quirke.  Training and quality assurance for rectal cancer: 20 years of data is enough. , 2003, The Lancet. Oncology.

[13]  M W Vannier,et al.  Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates. , 2001, International journal of radiation oncology, biology, physics.

[14]  S. Kety.  The theory and applications of the exchange of inert gas at the lungs and tissues. , 1951, Pharmacological reviews.

[15]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[16]  Hany Farid,et al.  Medical image registration with partial data , 2006, Medical Image Anal..

[17]  Claudio Pollo,et al.  Atlas-Based Segmentation of Pathological Brains Using a Model of Tumor Growth , 2002, MICCAI.

[18]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  J. Malley,et al.  Colonic polyps: complementary role of computer-aided detection in CT colonography. , 2002, Radiology.

[20]  Benoit M. Dawant,et al.  Brain Atlas Deformation in the Presence of Large Space-occupying Tumors , 1999, MICCAI.

[21]  Martin Styner,et al.  Parametric estimate of intensity inhomogeneities applied to MRI , 2000, IEEE Transactions on Medical Imaging.

[22]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[23]  Zongmin Wu,et al.  Compactly supported positive definite radial functions , 1995 .

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

[25]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[26]  Anil K. Bera,et al.  A test for normality of observations and regression residuals , 1987 .

[27]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[28]  Michael Brady,et al.  Estimating the bias field of MR images , 1997, IEEE Transactions on Medical Imaging.

[29]  G. Elisabeta Marai,et al.  Super-resolution registration using tissue-classified distance fields , 2006, IEEE Transactions on Medical Imaging.

[30]  W A Murphy,et al.  Magnetic resonance imaging of abdominal and pelvic lymphadenopathy. , 1984, Radiology.

[31]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[32]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[33]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[34]  J. Hardcastle,et al.  Colorectal cancer , 1993, Europe Against Cancer European Commission Series for General Practitioners.

[35]  Neil Roberts,et al.  Automatic Analysis of Normal Brain Dissymmetry of Males and Females in MR Images , 1998, MICCAI.

[36]  Michael Brady,et al.  Multimodal Imaging Techniques for the Extraction of Detailed Geometrical and Physiological Information for Use in Multi-Scale Models of Colorectal Cancer and Treatment of Individual Patients , 2006 .

[37]  J M Brady,et al.  Image filtering techniques for medical image post-processing: an overview. , 2004, The British journal of radiology.

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

[39]  J. Thirion,et al.  Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences , 1999, IEEE Transactions on Medical Imaging.

[40]  Hyunjin Park,et al.  Adaptive registration using local information measures , 2004, Medical Image Anal..

[41]  Jan Wolber,et al.  Diffusion MRI for prediction of response of rectal cancer to chemoradiation , 2002, The Lancet.

[42]  W. Bradley,et al.  MRI: The Basics , 1997 .

[43]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[44]  B. Cedermark,et al.  Prognostic value of preoperative magnetic resonance imaging of the pelvis in rectal cancer , 2003, The British journal of surgery.

[45]  Nicholas Ayache,et al.  Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections , 2001, IEEE Transactions on Medical Imaging.

[46]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

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

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

[49]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[50]  Gary E. Christensen,et al.  Transitive Inverse-Consistent Manifold Registration , 2005, IPMI.

[51]  James S. Duncan,et al.  Model-based deformable surface finding for medical images , 1996, IEEE Trans. Medical Imaging.

[52]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  M. Brady,et al.  Simultaneous Registration and Landmark Detection , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[54]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[56]  Daniel Rueckert,et al.  Simultaneous Segmentation and Registration for Medical Image , 2004, MICCAI.

[57]  E. Kreyszig,et al.  Advanced Engineering Mathematics. , 1974 .

[58]  Lewis D. Griffin,et al.  Zen and the art of medical image registration: correspondence, homology, and quality , 2003, NeuroImage.

[59]  M. Brady,et al.  Circumferential Resection Margin Assessment on MRI of Rectal Cancer , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[60]  K. Zierler.  Theory of Use of Indicators to Measure Blood Flow and Extracellular Volume and Calculation of Transcapillary Movement of Tracers , 1963 .

[61]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

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

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

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

[65]  Hervé Delingette,et al.  Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation , 2005, IEEE Transactions on Medical Imaging.

[66]  Lawrence A. Crum,et al.  Therapeutic ultrasound: Surgery and drug delivery , 2003 .

[67]  Thomas L. Harman,et al.  Advanced Engineering Mathematics Using Matlab V.4 , 1996 .

[68]  Brendan J. Frey,et al.  Unwrapping of MR phase images using a Markov random field model , 2006, IEEE Transactions on Medical Imaging.

[69]  Anthony J. Yezzi,et al.  Anti-geometric diffusion for adaptive thresholding and fast segmentation , 2003, IEEE Trans. Image Process..

[70]  Michael Brady,et al.  Non-rigid Multimodal Image Registration Using Local Phase , 2004, MICCAI.

[71]  R. Gupta,et al.  Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI , 1993, IEEE Trans. Medical Imaging.

[72]  David J. Hawkes,et al.  Information Theoretic Similarity Measures in Non-rigid Registration , 2003, IPMI.

[73]  Milan Sonka,et al.  Robust active appearance models and their application to medical image analysis , 2005, IEEE Transactions on Medical Imaging.

[74]  Christos Davatzikos,et al.  Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI] , 1999, IEEE Transactions on Medical Imaging.

[75]  Jianhua Yao,et al.  CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections. , 2004, Radiology.

[76]  Siddharth Manay.  Applications of anti-geometric diffusion of computer vision : thresholding, segmentation, and distance functions , 2003 .