Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

In this work we propose a technique to automatically estimate circular cross-sections of the vessels in CT scans. First, a circular contour is extracted for each slice of the CT by using the Hough transform. Afterward, the locations of the circles are optimized by means of a parametric snake model, and those circles which best fit the contours of the vessels are selected by applying a robust quality criterion. Finally, this collection of circles is used to estimate the local probability density functions of the image intensity inside and outside the vessels. We present a large variety of experiments on CT scans which show the reliability of the proposed method.

[1]  Rangasami L. Kashyap,et al.  Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms , 1994, CVGIP Graph. Model. Image Process..

[2]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[3]  Pablo Irarrazaval,et al.  3D Quantification of Wall Shear Stress and Oscillatory Shear Index Using a Finite-Element Method in 3D CINE PC-MRI Data of the Thoracic Aorta , 2016, IEEE Transactions on Medical Imaging.

[4]  Ronald M. Summers,et al.  Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Ganga Karunamuni,et al.  VESGEN 2D: Automated, User‐Interactive Software for Quantification and Mapping of Angiogenic and Lymphangiogenic Trees and Networks , 2009, Anatomical record.

[6]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  Miguel Angel Luengo-Oroz,et al.  Crowdsourcing Malaria Parasite Quantification: An Online Game for Analyzing Images of Infected Thick Blood Smears , 2012, Journal of medical Internet research.

[8]  Marilena Preda,et al.  Interactive 3D Analysis of Blood Vessel Trees and Collateral Vessel Volumes in Magnetic Resonance Angiograms in the Mouse Ischemic Hindlimb Model , 2013, The open medical imaging journal.

[9]  Yoshihiro Sakamoto,et al.  Virtual liver resection: computer-assisted operation planning using a three-dimensional liver representation , 2013, Journal of hepato-biliary-pancreatic sciences.

[10]  ichard,et al.  Simultaneous skeletonization and graph description of airway trees in 3 D CT images , 2015 .

[11]  Nazim Haouchine,et al.  Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery , 2015, Annals of Biomedical Engineering.

[12]  Maximilien Vermandel,et al.  Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results , 2013, International Journal of Computer Assisted Radiology and Surgery.

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

[14]  Lena Maier-Hein,et al.  Can Masses of Non-Experts Train Highly Accurate Image Classifiers? - A Crowdsourcing Approach to Instrument Segmentation in Laparoscopic Images , 2014, MICCAI.

[15]  Eduard Gröller,et al.  The VesselGlyph: focus & context visualization in CT-angiography , 2004, IEEE Visualization 2004.

[16]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[17]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Stephane Cotin,et al.  Modeling and Real-Time Simulation of a Vascularized Liver Tissue , 2012, MICCAI.

[19]  Samy Bengio,et al.  Large Scale Online Learning of Image Similarity through Ranking , 2009, IbPRIA.

[20]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[21]  Lena Maier-Hein,et al.  Crowdsourcing for Reference Correspondence Generation in Endoscopic Images , 2014, MICCAI.

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

[23]  Wallapak Tavanapong,et al.  Cable Footprint History: Spatio-Temporal Technique for Instrument Detection in Gastrointestinal Endoscopic Procedures , 2015, ICIP 2015.

[24]  Bin Zheng,et al.  Research Paper: Enhancing Text Categorization with Semantic-enriched Representation and Training Data Augmentation , 2006, J. Am. Medical Informatics Assoc..

[25]  Charles A. Taylor,et al.  Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty , 2015, IEEE Transactions on Medical Imaging.

[26]  Jung-Hwan Oh,et al.  Near Real-Time Retroflexion Detection in Colonoscopy , 2013, IEEE Journal of Biomedical and Health Informatics.

[27]  Ghassan Hamarneh,et al.  Live-Vessel: Extending Livewire for Simultaneous Extraction of Optimal Medial and Boundary Paths in Vascular Images , 2007, MICCAI.

[28]  David A. Steinman,et al.  A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms , 2009, IEEE Transactions on Medical Imaging.

[29]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[30]  Ya Zhang,et al.  Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).