Location registration and recognition (LRR) for serial analysis of nodules in lung CT scans

In the clinical workflow for lung cancer management, the comparison of nodules between CT scans from subsequent visits by a patient is necessary for timely classification of pulmonary nodules into benign and malignant and for analyzing nodule growth and response to therapy. The algorithm described in this paper takes (a) two temporally-separated CT scans, I(1) and I(2), and (b) a series of nodule locations in I(1), and for each location it produces an affine transformation that maps the locations and their immediate neighborhoods from I(1) to I(2). It does this without deformable registration and without initialization by global affine registration. Requiring the nodule locations to be specified in only one volume provides the clinician more flexibility in investigating the condition of the lung. The algorithm uses a combination of feature extraction, indexing, refinement, and decision processes. Together, these processes essentially "recognize" the neighborhoods. We show on lung CT scans that our technique works at near interactive speed and that the median alignment error of 134 nodules is 1.70mm compared to the error 2.14mm of the Diffeomorphic Demons algorithm, and to the error 3.57mm of the global nodule registration with local refinement. We demonstrate on the alignment of 250 nodules, that the algorithm is robust to changes caused by cancer progression and differences in breathing states, scanning procedures, and patient positioning. Our algorithm may be used both for diagnosis and treatment monitoring of lung cancer. Because of the generic design of the algorithm, it might also be used in other applications that require fast and accurate mapping of regions.

[1]  Michal Perdoch,et al.  Efficient sequential correspondence selection by cosegmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Geoffrey McLennan,et al.  Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. , 2003, Academic radiology.

[3]  B. J Hne,et al.  Spatio - temporal Image Processing: Theory and Scientific Applications , 1991 .

[4]  Sven Kabus,et al.  Performance study of a globally elastic locally rigid matching algorithm for follow-up chest CT , 2008, SPIE Medical Imaging.

[5]  Julien Jomier,et al.  Registration and Analysis of Vascular Images , 2003, International Journal of Computer Vision.

[6]  Dinggang Shen,et al.  Classification of Structural Images via High-Dimensional Image Warping, Robust Feature Extraction, and SVM , 2005, MICCAI.

[7]  Antonio Torralba,et al.  Nonparametric scene parsing: Label transfer via dense scene alignment , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Chia-Ling Tsai,et al.  Registration of Challenging Image Pairs: Initialization, Estimation, and Decision , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Anand Rangarajan,et al.  Unsupervised learning of an Atlas from unlabeled point-sets , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Karl Rohr.  On 3D differential operators for detecting point landmarks , 1997, Image Vis. Comput..

[12]  Karl Rohr,et al.  Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models , 2006, Medical Image Anal..

[13]  Nicholas Ayache,et al.  An interactive hybrid non-rigid registration framework for 3D medical images , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[14]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[15]  Lubomir M. Hadjiiski,et al.  Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching. , 2007, Medical physics.

[16]  Jiantao Pu,et al.  Pulmonary nodule registration: rigid or nonrigid? , 2011, Medical physics.

[17]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  Josien P. W. Pluim,et al.  Semi-automatic Reference Standard Construction for Quantitative Evaluation of Lung CT Registration , 2008, MICCAI.

[20]  Timothy F. Cootes,et al.  Groupwise Diffeomorphic Non-rigid Registration for Automatic Model Building , 2004, ECCV.

[21]  Horst Bischof,et al.  Automatic Point Landmark Matching for Regularizing Nonlinear Intensity Registration: Application to Thoracic CT Images , 2006, MICCAI.

[22]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[23]  W. Eric L. Grimson,et al.  Intra-patient Prone to Supine Colon Registration for Synchronized Virtual Colonoscopy , 2002, MICCAI.

[24]  Binsheng Zhao,et al.  Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. , 2009, Radiology.

[25]  Julien Jomier,et al.  Vascular Atlas Formation Using a Vessel-to-Image Affine Registration Method , 2003, MICCAI.

[26]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[27]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.

[28]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation by Image Exploration , 2004, ECCV.

[29]  Luc Soler,et al.  Portal Vein Registration for the Follow-Up of Hepatic Tumours , 2004, MICCAI.

[30]  Gregory D. Hager,et al.  A Meta Registration Framework for Lesion Matching , 2009, MICCAI.

[31]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[32]  Nathan D. Cahill,et al.  A Demons Algorithm for Image Registration with Locally Adaptive Regularization , 2009, MICCAI.

[33]  Andrew Zisserman,et al.  Multiple View Geometry , 1999 .

[34]  Antoni B. Chan,et al.  On measuring the change in size of pulmonary nodules , 2006, IEEE Transactions on Medical Imaging.

[35]  Jing Hua,et al.  3D Surface Matching and Registration through Shape Images , 2008, MICCAI.

[36]  Ayman El-Baz,et al.  Toward Early Diagnosis of Lung Cancer , 2009, MICCAI.

[37]  Gady Agam,et al.  Vessel tree reconstruction in thoracic CT scans with application to nodule detection , 2005, IEEE Transactions on Medical Imaging.

[38]  Charles V. Stewart,et al.  Simultaneous Covariance Driven Correspondence (CDC) and Transformation Estimation in the Expectation Maximization Framework , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[39]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[40]  Cristian Lorenz,et al.  Validation and comparison of registration methods for free-breathing 4D lung CT , 2008, SPIE Medical Imaging.

[41]  Xiaolei Huang,et al.  Robust Click-Point Linking: Matching Visually Dissimilar Local Regions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Karl Rohr,et al.  Evaluation of 3D Operators for the Detection of Anatomical Point Landmarks in MR and CT Images , 2002, Comput. Vis. Image Underst..

[43]  L P Clarke,et al.  The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software , 2008, Clinical pharmacology and therapeutics.

[44]  James V. Miller,et al.  MUSE: robust surface fitting using unbiased scale estimates , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[45]  Horst Bischof,et al.  A Duality Based Algorithm for TV- L 1-Optical-Flow Image Registration , 2007, MICCAI.

[46]  George K. Matsopoulos,et al.  Thoracic non-rigid registration combining self-organizing maps and radial basis functions , 2005, Medical Image Anal..

[47]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.

[48]  Tsuhan Chen,et al.  Soft shape context for iterative closest point registration , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[49]  Stephen R. Aylward,et al.  Tissue-Based Affine Registration of Brain Images to form a Vascular Density Atlas , 2003, MICCAI.

[50]  Milan Sonka,et al.  Matching and anatomical labeling of human airway tree , 2005, IEEE Transactions on Medical Imaging.

[51]  H. Bischof,et al.  A Framework for Comparison and Evaluation of Nonlinear Intra-Subject Image Registration Algorithms , 2007, The Insight Journal.

[52]  Charles V. Stewart,et al.  Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-linear Intensity Variations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[53]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[54]  Thomas Lange,et al.  Augmenting Intraoperative 3D Ultrasound with Preoperative Models for Navigation in Liver Surgery , 2004, MICCAI.

[55]  Richard C. Pais,et al.  Evaluation of Lung MDCT Nodule Annotation Across Radiologists and Methods 1 , 2006 .

[56]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[57]  Horst Bischof,et al.  SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images , 2006, CVAMIA.

[58]  Long Quan,et al.  A quasi-dense approach to surface reconstruction from uncalibrated images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  M. O'Boyle.  Spiral and Multislice Computed Tomography of the Body , 2003 .

[60]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[61]  M. L. R. D. Christenson,et al.  Guidelines for Management of Small Pulmonary Nodules Detected on CT Scans: A Statement From the Fleischner Society , 2006 .

[62]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[63]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  Dinggang Shen,et al.  Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels , 2004, IEEE Transactions on Medical Imaging.

[65]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[66]  Bernd Jähne,et al.  Spatio-Temporal Image Processing , 1993, Lecture Notes in Computer Science.

[67]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[68]  Noboru Niki,et al.  Analysis of Pulmonary Nodule Evolutions Using a Sequence of Three-Dimensional Thoracic CT Images , 2001, MICCAI.

[69]  Peter Meer,et al.  ROBUST TECHNIQUES FOR COMPUTER VISION , 2004 .

[70]  David Sarrut,et al.  Lung Deformation Estimation with Non-rigid Registration for Radiotherapy Treatment , 2003, MICCAI.

[71]  W. Scott,et al.  Treatment of non-small cell lung cancer stage I and stage II: ACCP evidence-based clinical practice guidelines (2nd edition). , 2007, Chest.

[72]  Marleen de Bruijne,et al.  Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT , 2008, MICCAI.

[73]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[74]  Charles V. Stewart,et al.  Location Registration and Recognition (LRR) for Longitudinal Evaluation of Corresponding Regions in CT Volumes , 2008, MICCAI.

[75]  Sven Kabus,et al.  Estimation of Organ Motion from 4D CT for 4D Radiation Therapy Planning of Lung Cancer , 2004, MICCAI.

[76]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[77]  Isabelle Bloch,et al.  Explicit Incorporation of Prior Anatomical Information Into a Nonrigid Registration of Thoracic and Abdominal CT and 18-FDG Whole-Body Emission PET Images , 2007, IEEE Transactions on Medical Imaging.

[78]  Nicholas Ayache,et al.  Non-parametric Diffeomorphic Image Registration with the Demons Algorithm , 2007, MICCAI.

[79]  Margrit Betke,et al.  Landmark detection in the chest and registration of lung surfaces with an application to nodule registration , 2003, Medical Image Anal..

[80]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[81]  James S. Duncan,et al.  Medical Image Analysis , 1999, IEEE Pulse.