Multi‐atlas segmentation of the skeleton from whole‐body MRI—Impact of iterative background masking

To improve multi‐atlas segmentation of the skeleton from whole‐body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation.

[1]  W. J. Langford.  Statistical Methods , 1959, Nature.

[2]  D. Collins,et al.  Therapy monitoring of skeletal metastases with whole‐body diffusion MRI , 2014, Journal of magnetic resonance imaging : JMRI.

[3]  Anders Hedström.  Automatic Segmentation of Skeleton in Whole-Body MR Images , 2013 .

[4]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[5]  Stefan Klein,et al.  The pythagorean averages as group images in efficient groupwise registration , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[6]  T. Takahara,et al.  Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display. , 2004, Radiation medicine.

[7]  M. Reiser,et al.  Role of MRI for the diagnosis and prognosis of multiple myeloma. , 2005, European journal of radiology.

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

[9]  Sébastien Ourselin,et al.  STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation , 2013, Medical Image Anal..

[10]  A. Ahmadian,et al.  Evaluation of whole‐body MR to CT deformable image registration , 2013, Journal of applied clinical medical physics.

[11]  T. Pabst,et al.  Whole-body MRI of multiple myeloma: comparison of different MRI sequences in assessment of different growth patterns. , 2009, European journal of radiology.

[12]  Laurence Collette,et al.  Can whole-body magnetic resonance imaging with diffusion-weighted imaging replace Tc 99m bone scanning and computed tomography for single-step detection of metastases in patients with high-risk prostate cancer? , 2012, European urology.

[13]  Stefan Klein,et al.  Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach , 2011, Medical Image Anal..

[14]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[15]  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.

[16]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[17]  Habib Zaidi,et al.  Comparison of atlas‐based techniques for whole‐body bone segmentation , 2017, Medical Image Anal..

[18]  N. Michoux,et al.  Whole-body 3D T1-weighted MR imaging in patients with prostate cancer: feasibility and evaluation in screening for metastatic disease. , 2015, Radiology.

[19]  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.

[20]  Jitendra Malik,et al.  Anisotropic Diffusion , 1994, Geometry-Driven Diffusion in Computer Vision.

[21]  J. Felblinger,et al.  omparative study of two whole-body imaging techniques in the case of elanoma metastases : Advantages of multi-contrast MRI examination ncluding a diffusion-weighted sequence in comparison with PET-CT , 2010 .

[22]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[23]  Torsten Rohlfing,et al.  Quo Vadis, Atlas-Based Segmentation? , 2005 .

[24]  Robert Jeraj,et al.  Development and evaluation of an articulated registration algorithm for human skeleton registration , 2014, Physics in medicine and biology.

[25]  Mert R. Sabuncu,et al.  Multi-atlas segmentation of biomedical images: A survey , 2014, Medical Image Anal..

[26]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[27]  D. Collins,et al.  Assessment of Treatment Response by Total Tumor Volume and Global Apparent Diffusion Coefficient Using Diffusion-Weighted MRI in Patients with Metastatic Bone Disease: A Feasibility Study , 2014, PloS one.

[28]  Daniel Rueckert,et al.  Similarity Metrics for Groupwise Non-rigid Registration , 2007, MICCAI.

[29]  Filip Malmberg,et al.  A concept for holistic whole body MRI data analysis, Imiomics , 2017, PloS one.

[30]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[31]  Carlos Ortiz-de-Solorzano,et al.  Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.

[32]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[33]  Jef Vandemeulebroucke,et al.  Registration strategies for multi‐modal whole‐body MRI mosaicing , 2018, Magnetic resonance in medicine.