Automatic Vertebrae Localization in Spine CT using Decision Forests
|
|
- Jasper Dawson
- 5 years ago
- Views:
Transcription
1 Automatic Vertebrae Localization in Spine CT using Decision Forests 1, Angel Alberich-Bayarri 1,2, Belén Fos-Guarinos 1, Fabio García-Castro 1, Luis Martí-Bonmatí 1,3 1 QUIBIM S.L., Valencia, Spain 2 La Fe Health Research Institute,Valencia, Spain 3 La Fe Radiology Department,Valencia, Spain
2 Outline Introduction Purpose Materials and methods Decision Forests Dataset Detection based on Regression Forests Refinement based on the spinal canal detection Results Conclusions
3 Introduction In clinical routine practice, when dealing with spinal abnormalities and pathologies, the localization and identification of vertebral bodies is a crucial step for an appropriate clinical diagnosis, surgical planning and follow-up assessments.
4 Purpose Nowadays, vertebrae identification is a manual task that hinders radiologists workflow Create pipelines to locate and identify automatically vertebrae in CT scans to help radiologists to perform diagnosis in a shorter period of time
5 DECISION TREE Supervised learning: annotated training data Input data: intensity-based features from different voxels of the image Internal (split) node Root node Output data: distance from the voxels to each vertebra Terminal (leaf) node
6 DECISION FOREST Tree parameters optimized over a randomly sampled subset of all possible features to minimize high fitting bias Ensemble of different trees to reduce overfitting The forest output is the average of all tree outputs
7
8
9 DATASET Dataset preparation for training the forest algorithm 232 spine CT scans o o 80% training 20% testing Arbitrary field of view Healthy and pathological cases
10 - Position (x,y,z coordinates) - Vertebral body name
11
12 FEATURE EXTRACTION 30,000 randomly-selected training voxels from the filed-of-view of the image. 40 x 40 x 120 mm 3 patch around each voxel. Each patch is divided into 10 x 10 x 30 mm 3 blocks. Mean intensity calculation from each box. 256 features associated to each training voxel.
13
14 Random Regression Forest Training f 1,1 f 2,1 f 3,1.... f 256,1 d T1,1 d T2,1 d T3,1.... d S1,1 f 1,2 f 2,2 f 3,2.... f 256,2 d T1,2 d T2,2 d T3,2.... d S1,2 f 1,3 f 2,3 f 3,3.... f 256, d T1,3 d T2,3 d T3,3.... d S1, d i = φ(fi) REGRESSION FOREST TRAINING f 1,n f 2,n f 3,n.... f 256,n d T1,n d T2,n d T3,n.... d S1,n
15 Random Regression Forest Testing Unseen CT scan Feature Extraction RRF Testing Centroid Estimation c i = d i + X i
16 High variability in spine curvatures Refinement step to adapt the centroid detection to the patient-specific vertebrae position SPINAL CANNAL DETECTION
17 Refinement Based on the Spinal Canal Detection
18 Refinement Based on the Spinal Canal Detection Predicted vertebrae position after RRF Predicted vertebrae position after refinement Real position
19 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5 S1 T1 T2 T3 T4 T4 T6 T7 T8 T9 T10 T11 T12 L1 L2 L3 L4 L5 S1 Identification Rate (%) Median localization error (mm) Results Vertebra x y z
20 Results Region Median (mm) Mean (mm) STD (mm) Id. Rate All % Thoracic % Lumbar %
21 Conclusions We developed a method that provides the lowest error in the automatic detection and identification of vertebrae in CT scans. Vertebrae identification can be addressed on arbitrary field-ofview scans. This improves the radiological workflow in spine evaluation through computed tomography and allows the creation of automatic pipelines for the calculation of vertebrae bone microarchitecture characteristics.
22 Acknowledgements Ana Penadés Economic & Financial Manager Administrat ion Enrique Ruiz CTO Developme nt Alejandro Rodríguez PhD Image Analysis Engineer Amadeo Ten Image Analysis Engineer Imaging Biomarkers Analysis Sara Carratalá Neuroradiolo gy Sandra Pérez Data Manager Francisco Alcaide MRI Technician & PREBI Clinical Trials & PREBI Ángel Alberich- Bayarri, PhD. GIBI Scientific Technical Director & QUIBIM Founder and CEO Luis Martí Bonmatí MD, PhD. GIBI General Director & QUIBIM Founder GIBI230 & QUIBIM Directors Mª Carmen Rodríguez Team Coordinator Katherine Wilisch Marketing & Communicati ons Manager Encarna Sánchez Business Developer & Project Manager Rafael Hernández Navarro CTO Alejandro Mañas Full Stack Senior Developer Fabio García Castro R&D Responsible NEURO & MSK Alfredo Torregros a Image Analysis Scientist ONCO Belén Fos Guarinos Image Analysis Scientist LUNG Ana Jiménez Pastor Image Analysis Scientist LIVER Irene Mayorga Clinical Trials Coordinator Raúl Yébana Image Analysis Technician Management Development R+D and Imaging Biomarkers Analysis Clinical Trials
23 Automatic Vertebrae Localization in Spine CT using Decision Forests 1, Angel Alberich-Bayarri 1,2, Belén Fos-Guarinos 1, Fabio García-Castro 1, Luis Martí-Bonmatí 1,3 1 QUIBIM S.L., Valencia, Spain 2 La Fe Health Research Institute,Valencia, Spain 3 La Fe Radiology Department,Valencia, Spain
Automatic Vertebrae Localization in Pathological Spine CT using Decision Forests
Automatic Vertebrae Localization in Pathological Spine CT using Decision Forests Ana Jiménez-Pastor 1, Esther Tomás-González 1, Ángel Alberich-Bayarri 1,2, Fabio García-Castro 1, David García-Juan 1, Luis
More informationMachine Learning for Medical Image Analysis. A. Criminisi
Machine Learning for Medical Image Analysis A. Criminisi Overview Introduction to machine learning Decision forests Applications in medical image analysis Anatomy localization in CT Scans Spine Detection
More informationarxiv: v1 [cs.cv] 17 May 2017
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization arxiv:1705.05998v1 [cs.cv] 17 May 2017 Dong Yang 1, Tao Xiong 2, Daguang
More informationVertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations
Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations B. Glocker 1, D. Zikic 1, E. Konukoglu 2, D. R. Haynor 3, A. Criminisi 1 1 Microsoft Research, Cambridge,
More informationVertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations
Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations Ben Glocker 1,DarkoZikic 1, Ender Konukoglu 2, David R. Haynor 3, and Antonio Criminisi 1 1 Microsoft Research,
More informationIterative fully convolutional neural networks for automatic vertebra segmentation
Iterative fully convolutional neural networks for automatic vertebra segmentation Nikolas Lessmann Image Sciences Institute University Medical Center Utrecht Pim A. de Jong Department of Radiology University
More informationarxiv: v1 [cs.cv] 13 Mar 2017
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets Anjany Sekuboyina 1,2,, Alexander Valentinitsch 2, Jan S. Kirschke 2, and Bjoern H. Menze 1 arxiv:1703.04347v1
More informationDeep Learning in Pulmonary Image Analysis with Incomplete Training Samples
Deep Learning in Pulmonary Image Analysis with Incomplete Training Samples Ziyue Xu, Staff Scientist, National Institutes of Health Nov. 2nd, 2017 (GTC DC Talk DC7137) Image Analysis Arguably the most
More informationHierarchical Multi structure Segmentation Guided by Anatomical Correlations
Hierarchical Multi structure Segmentation Guided by Anatomical Correlations Oscar Alfonso Jiménez del Toro oscar.jimenez@hevs.ch Henning Müller henningmueller@hevs.ch University of Applied Sciences Western
More informationMedical Image Segmentation
Medical Image Segmentation Xin Yang, HUST *Collaborated with UCLA Medical School and UCSB Segmentation to Contouring ROI Aorta & Kidney 3D Brain MR Image 3D Abdominal CT Image Liver & Spleen Caudate Nucleus
More informationAutomatic Vertebra Detection in X-Ray Images
Automatic Vertebra Detection in X-Ray Images Daniel C. Moura INEB - Instituto de Engenharia Biomédica, Laboratório de Sinal e Imagem, Porto, Portugal Instituto Politécnico de Viana do Castelo, Escola Superior
More informationA Workflow for Improving Medical Visualization of Semantically Annotated CT-Images
A Workflow for Improving Medical Visualization of Semantically Annotated CT-Images Alexander Baranya 1,2, Luis Landaeta 1,2, Alexandra La Cruz 1, and Maria-Esther Vidal 2 1 Biophysic and Bioengeneering
More informationDeep Learning for Automatic Localization, Identification, and Segmentation of Vertebral Bodies in Volumetric MR Images
Deep Learning for Automatic Localization, Identification, and Segmentation of Vertebral Bodies in Volumetric MR Images Amin Suzani a, Abtin Rasoulian a, Alexander Seitel a,sidneyfels a, Robert N. Rohling
More informationRib Cage Segmentation in CT Scans
1. Rib Cage Segmentation Literature Review Biomedical Image Science 14 October 2013 Enrico Contini 3913511 Supervisor: Dr. Josien Pluim Daily Supervisor: Yolanda Noorda Table of Contents 1. Introduction...
More informationMedGIFT projects in medical imaging. Henning Müller
MedGIFT projects in medical imaging Henning Müller Where we are 2 Who I am Medical informatics studies in Heidelberg, Germany (1992-1997) Exchange with Daimler Benz research, USA PhD in image processing,
More informationMEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 20: Machine Learning in Medical Imaging II (deep learning and decision forests)
SPRING 2016 1 MEDICAL IMAGE COMPUTING (CAP 5937) LECTURE 20: Machine Learning in Medical Imaging II (deep learning and decision forests) Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV),
More informationSemi-Automatic Detection of Cervical Vertebrae in X-ray Images Using Generalized Hough Transform
Semi-Automatic Detection of Cervical Vertebrae in X-ray Images Using Generalized Hough Transform Mohamed Amine LARHMAM, Saïd MAHMOUDI and Mohammed BENJELLOUN Faculty of Engineering, University of Mons,
More informationA SURVEY ON LIVER TUMOR DETECTION AND SEGMENTATION METHODS
A SURVEY ON LIVER TUMOR DETECTION AND SEGMENTATION METHODS R. Rajagopal 1 and P. Subbaiah 2 1 Department of Electronics and Communication Engineering, St. Peter University, Avadi, Chennai, Tamilnadu, India
More informationUsing Probability Maps for Multi organ Automatic Segmentation
Using Probability Maps for Multi organ Automatic Segmentation Ranveer Joyseeree 1,2, Óscar Jiménez del Toro1, and Henning Müller 1,3 1 University of Applied Sciences Western Switzerland (HES SO), Sierre,
More informationMICRO CT LUNG SEGMENTATION. Using Analyze
MICRO CT LUNG SEGMENTATION Using Analyze 2 Table of Contents 1. Introduction page 3 2. Lung Segmentation page 4 3. Lung Volume Measurement page 13 4. References page 16 3 Introduction Mice are often used
More informationEvaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging
Evaluation of 1D, 2D and 3D nodule size estimation by radiologists for spherical and non-spherical nodules through CT thoracic phantom imaging Nicholas Petrick, Hyun J. Grace Kim, David Clunie, Kristin
More informationProstate Detection Using Principal Component Analysis
Prostate Detection Using Principal Component Analysis Aamir Virani (avirani@stanford.edu) CS 229 Machine Learning Stanford University 16 December 2005 Introduction During the past two decades, computed
More informationReal World Experience: Developing Dose and Protocol Monitoring from Scratch
Real World Experience: Developing Dose and Protocol Monitoring from Scratch Ingrid Reiser, PhD DABR Department of Radiology The University of Chicago Outline CT protocol monitoring Let s build a protocol
More informationKinematic Analysis of Lumbar Spine Undergoing Extension and Dynamic Neural Foramina Cross Section Measurement
Copyright c 2008 ICCES ICCES, vol.7, no.2, pp.57-62 Kinematic Analysis of Lumbar Spine Undergoing Extension and Dynamic Neural Foramina Cross Section Measurement Yongjie Zhang 1,BoyleC.Cheng 2,ChanghoOh
More informationAC : APPLICATION OF PARAMETRIC SOLID MODELING FOR ORTHOPEDIC STUDIES OF THE HUMAN SPINE
AC 2011-2785: APPLICATION OF PARAMETRIC SOLID MODELING FOR ORTHOPEDIC STUDIES OF THE HUMAN SPINE Jorge Rodriguez, Western Michigan University Jorge Rodriguez is an Associate Professor in the Department
More informationMedical Imaging Projects
NSF REU MedIX Summer 2006 Medical Imaging Projects Daniela Stan Raicu, PhD http://facweb.cs.depaul.edu/research draicu@cs.depaul.edu Outline Medical Informatics Imaging Modalities Computed Tomography Medical
More informationIn vivo Bone Characterization from Magnetic Resonance Imaging: Morphometry and Mechanical analysis
In vivo Bone Characterization from Magnetic Resonance Imaging: Morphometry and Mechanical analysis Angel Alberich-Bayarri, PhD angel@quibim.com 15 th october 2014 La Fe Polytechnics and University Hospital
More informationAutomatic Labeling and Segmentation of Vertebrae in CT Images
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
More informationApplying Boosting Algorithm for Improving Diagnosis of Interstitial Lung Diseases
CS 229 Machine Learning Life Sciences Project Jason Yang SUNet ID: jason2 Applying Boosting Algorithm for Improving Diagnosis of Interstitial Lung Diseases Background and Related Work Interstitial Lung
More informationVertebrae Segmentation in 3D CT Images based on a Variational Framework
Vertebrae Segmentation in 3D CT Images based on a Variational Framework Kerstin Hammernik, Thomas Ebner, Darko Stern, Martin Urschler, and Thomas Pock Abstract Automatic segmentation of 3D vertebrae is
More informationExtraction and recognition of the thoracic organs based on 3D CT images and its application
1 Extraction and recognition of the thoracic organs based on 3D CT images and its application Xiangrong Zhou, PhD a, Takeshi Hara, PhD b, Hiroshi Fujita, PhD b, Yoshihiro Ida, RT c, Kazuhiro Katada, MD
More informationAutomated segmentation methods for liver analysis in oncology applications
University of Szeged Department of Image Processing and Computer Graphics Automated segmentation methods for liver analysis in oncology applications Ph. D. Thesis László Ruskó Thesis Advisor Dr. Antal
More informationRandom Forest Classification and Attribute Selection Program rfc3d
Random Forest Classification and Attribute Selection Program rfc3d Overview Random Forest (RF) is a supervised classification algorithm using multiple decision trees. Program rfc3d uses training data generated
More information8/3/2017. Contour Assessment for Quality Assurance and Data Mining. Objective. Outline. Tom Purdie, PhD, MCCPM
Contour Assessment for Quality Assurance and Data Mining Tom Purdie, PhD, MCCPM Objective Understand the state-of-the-art in contour assessment for quality assurance including data mining-based techniques
More informationContext-sensitive Classification Forests for Segmentation of Brain Tumor Tissues
Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues D. Zikic, B. Glocker, E. Konukoglu, J. Shotton, A. Criminisi, D. H. Ye, C. Demiralp 3, O. M. Thomas 4,5, T. Das 4, R. Jena
More informationEDITORS. Proceedings of the Workshop on Applied Topological Structures WATS'17. Editorial Universitat Politècnica de València
EDITORS Josefa Marín and Jesús Rodríguez-López Proceedings of the Workshop on Applied Topological Structures WATS'17 Editorial Universitat Politècnica de València Congress UPV Proceedings of the Workshop
More informationComputer-Aided Detection system for Hemorrhage contained region
Computer-Aided Detection system for Hemorrhage contained region Myat Mon Kyaw Faculty of Information and Communication Technology University of Technology (Yatanarpon Cybercity), Pyin Oo Lwin, Myanmar
More informationVisualisation : Lecture 1. So what is visualisation? Visualisation
So what is visualisation? UG4 / M.Sc. Course 2006 toby.breckon@ed.ac.uk Computer Vision Lab. Institute for Perception, Action & Behaviour Introducing 1 Application of interactive 3D computer graphics to
More informationAutomatic Vascular Tree Formation Using the Mahalanobis Distance
Automatic Vascular Tree Formation Using the Mahalanobis Distance Julien Jomier, Vincent LeDigarcher, and Stephen R. Aylward Computer-Aided Diagnosis and Display Lab, Department of Radiology The University
More informationRule-Based Ventral Cavity Multi-organ Automatic Segmentation in CT Scans
Rule-Based Ventral Cavity Multi-organ Automatic Segmentation in CT Scans Assaf B. Spanier (B) and Leo Joskowicz The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University
More informationAutomated Model-Based Rib Cage Segmentation and Labeling in CT Images
Automated Model-Based Rib Cage Segmentation and Labeling in CT Images Tobias Klinder 1,2,CristianLorenz 2,JensvonBerg 2, Sebastian P.M. Dries 2, Thomas Bülow 2,andJörn Ostermann 1 1 Institut für Informationsverarbeitung,
More informationCURRICULUM COMMITTEE MEETING Friday, March 18, :00 p.m. Student Life Center, Faculty Dining Room (Building 23, First Floor) AGENDA
CURRICULUM COMMITTEE MEETING Friday, March 18, 2016-2:00 p.m. Student Life Center, Faculty Dining Room (Building 23, First Floor) I. Call to Order AGENDA II. Roll Call III. Minutes of meeting of January
More informationA Generation Methodology for Numerical Phantoms with Statistically Relevant Variability of Geometric and Physical Properties
A Generation Methodology for Numerical Phantoms with Statistically Relevant Variability of Geometric and Physical Properties Steven Dolly 1, Eric Ehler 1, Yang Lou 2, Mark Anastasio 2, Hua Li 2 (1) University
More informationThE ultimate, INTuITIVE Mr INTErFAcE
ThE ultimate, INTuITIVE Mr INTErFAcE Empowering you to do more The revolutionary Toshiba M-power user interface takes Mr performance and flexibility to levels higher than ever before. M-power is able to
More informationIntroduction. Biomedical Image Analysis. Contents. Prof. Dr. Philippe Cattin. MIAC, University of Basel. Feb 22nd, of
Introduction Prof. Dr. Philippe Cattin MIAC, University of Basel Contents Abstract 1 Varia About Me About these Slides 2 My Research 2.1 Segmentation Segmentation of Facial Soft Tissues Segmentation of
More informationINTRODUCTION TO DATA MINING. Daniel Rodríguez, University of Alcalá
INTRODUCTION TO DATA MINING Daniel Rodríguez, University of Alcalá Outline Knowledge Discovery in Datasets Model Representation Types of models Supervised Unsupervised Evaluation (Acknowledgement: Jesús
More informationAutomatic Segmentation of the Lumbar Spine from Medical Images
University of Exeter Department of Physics Automatic Segmentation of the Lumbar Spine from Medical Images Hugo Winfield Hutt February, 2016 Submitted by Hugo Winfield Hutt, to the University of Exeter
More informationDetection and Identification of Lung Tissue Pattern in Interstitial Lung Diseases using Convolutional Neural Network
Detection and Identification of Lung Tissue Pattern in Interstitial Lung Diseases using Convolutional Neural Network Namrata Bondfale 1, Asst. Prof. Dhiraj Bhagwat 2 1,2 E&TC, Indira College of Engineering
More informationA Statistical Aspect of Imaging Analytics Based Computer-Aided Diagnosis
A Statistical Aspect of Imaging Analytics Based Computer-Aided Diagnosis Le Lu (01/2013) Radiology and Imaging Science National Institutes of Health Clinical Center le.lu@nih.gov Next decade will be very
More informationPredicting Semantic Descriptions from Medical Images with Convolutional Neural Networks
Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks Thomas Schlegl 1, Sebastian Waldstein 2, Wolf-Dieter Vogl 1,2, Ursula Schmidt-Erfurth 2, and Georg Langs 1 1 Computational
More informationA Robust Segmentation Framework for Spine Trauma Diagnosis
A Robust Segmentation Framework for Spine Trauma Diagnosis Poay Hoon Lim 1, Ulas Bagci 2 and Li Bai 1 1 School of Computer Science, University of Nottingham, UK 2 Radiology and Imaging Sciences, National
More informationFrequency split metal artifact reduction (FSMAR) in computed tomography
The Johns Hopkins University Advanced Computer Integrated Surgery Group 4 Metal Artifact Removal in C-arm Cone-Beam CT Paper Seminar Critical Review of Frequency split metal artifact reduction (FSMAR)
More informationFunctional Analysis of the Vertebral Column Based on MR and Direct Volume Rendering
Functional Analysis of the Vertebral Column Based on MR and Direct Volume Rendering P. Hastreiter 1, C. Rezk-Salama 2,K.Eberhardt 3 and B. Tomandl 3,andT.Ertl 4 1 Neurocenter, University of Erlangen-Nuremberg,
More information2012 SPIE (International Society for Optics and Photonics)
This is a reprint of material published in Proceedings of SPIE 8315 (Medical Imaging 2012: Computer-Aided Diagnosis), San Diego, California, USA, Feb. 2012, pp. 831513-1 8. 2012 SPIE (International Society
More informationDefinition of the evaluation protocol and goals for competition 2
www.visceral.eu Definition of the evaluation protocol and goals for competition 2 Deliverable number D4.2 Dissemination level Public Delivery date 7 February 2014 Status Author(s) Final Georg Langs, Bjoern
More informationChoosing a teleradiology provider
Choosing a teleradiology provider Tim Hunter, MD Professor Emeritus, Department of Medical Imaging University of Arizona Purpose of this talk Provide brief overview of teleradiology Provide a general guideline
More informationA DYNAMIC-IMAGE COMPUTATIONAL APPROACH FOR MODELING THE SPINE
A DYNAMIC-IMAGE COMPUTATIONAL APPROACH FOR MODELING THE SPINE by Md. Abedul Haque BS, Bangladesh University of Engineering and Technology, 2005 MS, Bangladesh University of Engineering and Technology,
More informationGeneration of Curved Planar Reformations from Magnetic Resonance Images of the Spine
Generation of Curved Planar Reformations from Magnetic Resonance Images of the Spine TomažVrtovec 1,2,Sébastien Ourselin 2,LavierGomes 3, Boštjan Likar 1,andFranjoPernuš 1 1 University of Ljubljana, Faculty
More informationIncreasing Interoperability, what is the Impact on Reliability? Illustrated with Health care examples
Illustrated with Health care examples by Gerrit Muller University of South-Eastern Norway-NISE e-mail: gaudisite@gmail.com www.gaudisite.nl Abstract In all domains the amount of interoperability between
More informationReviewing Radiology Results
AED14 Version 3.1 Emergency Department Operational Areas Included ED Doctors ED Consultants Roles Responsible for Carrying out this Process Operational Areas Excluded All areas other than the Emergency
More informationCTSI Module 8 Workshop Introduction to Biomedical Informatics, Part V
CTSI Module 8 Workshop Introduction to Biomedical Informatics, Part V Practical Tools: Data Processing & Analysis William Hsu, PhD Assistant Professor Medical Imaging Informatics Group Dept of Radiological
More information[PDR03] RECOMMENDED CT-SCAN PROTOCOLS
SURGICAL & PROSTHETIC DESIGN [PDR03] RECOMMENDED CT-SCAN PROTOCOLS WORK-INSTRUCTIONS DOCUMENT (CUSTOMER) RECOMMENDED CT-SCAN PROTOCOLS [PDR03_V1]: LIVE 1 PRESCRIBING SURGEONS Patient-specific implants,
More informationMichal E. Kulon, MD 1,2 1. Peter Komlosi, MD, PhD 3 3. Radiology Universe Institute, 2
Blend+Proximity, a novel algorithm achieves high suppression of metallic streak artifacts and maximal preservation of contrast between soft tissues and iodinated contrast material on dual-energy CT scans
More informationDetecting Bone Lesions in Multiple Myeloma Patients using Transfer Learning
Detecting Bone Lesions in Multiple Myeloma Patients using Transfer Learning Matthias Perkonigg 1, Johannes Hofmanninger 1, Björn Menze 2, Marc-André Weber 3, and Georg Langs 1 1 Computational Imaging Research
More informationCorrespondence Management with Medical Director. For Medical Director 3.15 or later
For Medical Director 3.15 or later This guide presents the various methods for importing correspondence into Medical Director, and helps you establish which method best suits your Practice s workflow.
More informationSTANDARDISATION IN DIGITAL PATHOLOGY. Marcial García Rojo Hospital de Jerez. Cádiz. España Vice-President Spanish Society for Health Informatics
STANDARDISATION IN DIGITAL PATHOLOGY Marcial García Rojo Hospital de Jerez. Cádiz. España Vice-President Spanish Society for Health Informatics Multiple image sources Introduction Digital imaging in pathology
More informationSeeing the Big Picture
Seeing the Big Picture Segmenting Images to Create Data 15.071x The Analytics Edge Image Segmentation Divide up digital images to salient regions/clusters corresponding to individual surfaces, objects,
More informationGE Healthcare. AppsLinq* remote courses catalogue
GE Healthcare AppsLinq* remote courses catalogue AppsLinq * remote training for MR Magnetic Resonance AppsLinq* a GE Training in Partnership (TiP) program, revolutionizes applications training with live,
More informationUniversity of Central Florida Class Specification Administrative and Professional. Director IT Financials Systems
Director IT Financials Systems Job Code: 2604 Serve as the top technical administrator for the enterprise Financials systems. Oversee programming, business systems analysis, documentation, and training
More informationarxiv: v1 [cs.cv] 11 Apr 2018
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning Takayasu Moriya a, Holger R. Roth a, Shota Nakamura b, Hirohisa Oda c, Kai Nagara c, Masahiro Oda a,
More informationAutomatic 3D Registration of Lung Surfaces in Computed Tomography Scans
Automatic 3D Registration of Lung Surfaces in Computed Tomography Scans Margrit Betke, PhD 1, Harrison Hong, BA 1, and Jane P. Ko, MD 2 1 Computer Science Department Boston University, Boston, MA 02215,
More informationIssues Regarding fmri Imaging Workflow and DICOM
Issues Regarding fmri Imaging Workflow and DICOM Lawrence Tarbox, Ph.D. Fred Prior, Ph.D Mallinckrodt Institute of Radiology Washington University in St. Louis What is fmri fmri is used to localize functions
More informationLocalisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting
Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting P.A. Bromiley, J.E. Adams and T.F. Cootes Imaging Sciences Research Group, University of Manchester,
More informationIntroduction to Artificial Intelligence
Introduction to Artificial Intelligence COMP307 Machine Learning 2: 3-K Techniques Yi Mei yi.mei@ecs.vuw.ac.nz 1 Outline K-Nearest Neighbour method Classification (Supervised learning) Basic NN (1-NN)
More informationIntuitionistic Fuzzy Clustering Based Segmentation of Spine MR Images
International Research Journal of Engineering and Technology (IRJET) e-issn: - Volume: 4 Issue: July - www.irjet.net p-issn: - Intuitionistic Fuzzy Clustering Based Segmentation of Spine MR Images Binita
More informationDeviceless respiratory motion correction in PET imaging exploring the potential of novel data driven strategies
g Deviceless respiratory motion correction in PET imaging exploring the potential of novel data driven strategies Presented by Adam Kesner, Ph.D., DABR Assistant Professor, Division of Radiological Sciences,
More informationDevelopment of 3D Model-based Morphometric Method for Assessment of Human Weight-bearing Joint. Taeho Kim
Development of 3D Model-based Morphometric Method for Assessment of Human Weight-bearing Joint Taeho Kim Introduction Clinical measurement in the foot pathology requires accurate and robust measurement
More informationConference Biomedical Engineering
Automatic Medical Image Analysis for Measuring Bone Thickness and Density M. Kovalovs *, A. Glazs Image Processing and Computer Graphics Department, Riga Technical University, Latvia * E-mail: mihails.kovalovs@rtu.lv
More informationPROCEEDINGS OF SPIE. Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks
PROCEEDINGS OF SPIE SPIEDigitalLibrary.org/conference-proceedings-of-spie Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks Fengxia Yan, Jayaram
More informationAutomatic Thoracic CT Image Segmentation using Deep Convolutional Neural Networks. Xiao Han, Ph.D.
Automatic Thoracic CT Image Segmentation using Deep Convolutional Neural Networks Xiao Han, Ph.D. Outline Background Brief Introduction to DCNN Method Results 2 Focus where it matters Structure Segmentation
More informationIntegrating information in Radiology: Teaching File System (TFS) implementation in a third-level hospital using MIRC
Integrating information in Radiology: Teaching File System (TFS) implementation in a third-level hospital using MIRC Poster No.: C-2964 Congress: ECR 2018 Type: Educational Exhibit Authors: S. Ibáñez Caturla,
More informationMultiple Sclerosis Brain MRI Segmentation Workflow deployment on the EGEE grid
Multiple Sclerosis Brain MRI Segmentation Workflow deployment on the EGEE grid Erik Pernod 1, Jean-Christophe Souplet 1, Javier Rojas Balderrama 2, Diane Lingrand 2, Xavier Pennec 1 Speaker: Grégoire Malandain
More informationIschemic Stroke Lesion Segmentation Proceedings 5th October 2015 Munich, Germany
0111010001110001101000100101010111100111011100100011011101110101101012 Ischemic Stroke Lesion Segmentation www.isles-challenge.org Proceedings 5th October 2015 Munich, Germany Preface Stroke is the second
More informationbetter images mean better results
better images mean better results A better way for YOU and YOUR patient brought to you by Advanced Neuro analysis with access to studies wherever you need it Advanced Neuro from Invivo Advancements in
More informationDetecting Thoracic Diseases from Chest X-Ray Images Binit Topiwala, Mariam Alawadi, Hari Prasad { topbinit, malawadi, hprasad
CS 229, Fall 2017 1 Detecting Thoracic Diseases from Chest X-Ray Images Binit Topiwala, Mariam Alawadi, Hari Prasad { topbinit, malawadi, hprasad }@stanford.edu Abstract Radiologists have to spend time
More informationUsing Machine Learning to Identify Security Issues in Open-Source Libraries. Asankhaya Sharma Yaqin Zhou SourceClear
Using Machine Learning to Identify Security Issues in Open-Source Libraries Asankhaya Sharma Yaqin Zhou SourceClear Outline - Overview of problem space Unidentified security issues How Machine Learning
More informationStatistical anatomical modelling for efficient and personalised spine biomechanical models
Statistical anatomical modelling for efficient and personalised spine biomechanical models I. Castro Mateos A thesis submitted for the degree of Doctor of Philosophy (PhD) 2016 Statistical anatomical modelling
More informationComputational Medical Imaging Analysis Chapter 4: Image Visualization
Computational Medical Imaging Analysis Chapter 4: Image Visualization Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky Lexington,
More informationEnsemble Learning: An Introduction. Adapted from Slides by Tan, Steinbach, Kumar
Ensemble Learning: An Introduction Adapted from Slides by Tan, Steinbach, Kumar 1 General Idea D Original Training data Step 1: Create Multiple Data Sets... D 1 D 2 D t-1 D t Step 2: Build Multiple Classifiers
More informationSupervoxel Classification Forests for Estimating Pairwise Image Correspondences
Supervoxel Classification Forests for Estimating Pairwise Image Correspondences Fahdi Kanavati 1, Tong Tong 1, Kazunari Misawa 2, Michitaka Fujiwara 3, Kensaku Mori 4, Daniel Rueckert 1, and Ben Glocker
More informationThe new AnyBody Modeling System & Musculoskeletal Model Repository
The new AnyBody Modeling System & Musculoskeletal Model Repository TOUR AND OVERVIEW OF THE NEW 7.1 VERS I ON Outline General introduction to the modeling system New features in the Modeling System Morten
More informationThe NeuroLOG Platform Federating multi-centric neuroscience resources
Software technologies for integration of process and data in medical imaging The Platform Federating multi-centric neuroscience resources Johan MONTAGNAT Franck MICHEL Vilnius, Apr. 13 th 2011 ANR-06-TLOG-024
More informationImproving Diagnostic Imaging
Improving Diagnostic Imaging February 2018 Brent Wagner, MD (President-Elect) Brent.Wagner@towerhealth.org Disclosure: Volunteer, American Board of Radiology Important Note All of the information being
More informationVessel Explorer: a tool for quantitative measurements in CT and MR angiography
Clinical applications Vessel Explorer: a tool for quantitative measurements in CT and MR angiography J. Oliván Bescós J. Sonnemans R. Habets J. Peters H. van den Bosch T. Leiner Healthcare Informatics/Patient
More informationManagement and Visualization of images with labeled segments:
Management and Visualization of images with labeled segments: Chest CT Atlas Management Anthony P. Reeves and Jaesung Lee Introduction to label map management. Currently the main label map is defined for
More informationMathematical Modeling of Thoracolumbar Spine using ANSYS
Mathematical Modeling of Thoracolumbar Spine using ANSYS Lee Kim Kheng, Qiu Tian Xia, Teo Ee Chon, Ng Hong Wan, Yang Kai Abstract NANYANG TECHNOLOGICAL UNIVERSITY Over the years, finite element method
More informationCHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE
32 CHAPTER 3 TUMOR DETECTION BASED ON NEURO-FUZZY TECHNIQUE 3.1 INTRODUCTION In this chapter we present the real time implementation of an artificial neural network based on fuzzy segmentation process
More informationIf you have forgotten your password, touch the forgot password button and follow the steps to recover your password.
Log In Logging in to SpineTech SOAP: On the left side of the screen you will find instructions for the initial login process after you have registered with SpineTech SOAP. 1. Enter your assigned clinic
More informationImproving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
1170 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 5, MAY 2016 Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation Holger R. Roth*, Le Lu, Senior Member,
More informationCOMPARISION OF NORMAL Vs HERNIATED CERVICAL IMAGES USING GRAY LEVEL TEXTURE FEATURES
COMPARISION OF NORMAL Vs HERNIATED CERVICAL IMAGES USING GRAY LEVEL TEXTURE FEATURES C.Malarvizhi 1 and P.Balamurugan 2 1 Ph.D Scholar, India 2 Assistant Professor,India Department Computer Science, Government
More information