Cover Image

Cloud-Based Benchmarking of Medical Image Analysis.

Bibliographic Details
Main Author: Hanbury, Allan.
Other Authors: Müller, Henning., Langs, Georg.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2017.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • Contributors
  • Acronyms
  • Part I Evaluation-as-a-Service
  • 1 VISCERAL: Evaluation-as-a-Service for Medical Imaging
  • 1.1 Introduction
  • 1.2 VISCERAL Benchmarks
  • 1.2.1 Anatomy Benchmarks
  • 1.2.2 Detection Benchmark
  • 1.2.3 Retrieval Benchmark
  • 1.3 Evaluation-as-a-Service in VISCERAL
  • 1.4 Main Outcomes of VISCERAL
  • 1.4.1 Gold Corpus
  • 1.4.2 Silver Corpus
  • 1.4.3 Evaluation Metric Calculation Software
  • 1.5 Experience with EaaS in VISCERAL
  • 1.6 Conclusion
  • References
  • 2 Using the Cloud as a Platform for Evaluation and Data Preparation
  • 2.1 Introduction
  • 2.2 VISCERAL Registration System
  • 2.2.1 Registration
  • 2.2.2 Participant Dashboard
  • 2.2.3 Management of Participants
  • 2.2.4 Open Source Software Release
  • 2.3 Continuous Evaluation in the Cloud
  • 2.3.1 Submission
  • 2.3.2 Isolation of the VM
  • 2.3.3 Initial Test
  • 2.3.4 Executing Algorithms and Saving the Results
  • 2.3.5 Evaluation of Results
  • 2.4 Cloud-Based Evaluation Infrastructure
  • 2.4.1 Setting up a Cloud Environment
  • 2.4.2 Setting up a Benchmark in the Cloud
  • 2.4.3 Cloud Set-Up for the VISCERAL Benchmarks
  • 2.4.4 Cloud Infrastructure Setup and Management Experience Report
  • 2.5 Conclusion
  • References
  • Part II VISCERAL Datasets
  • 3 Ethical and Privacy Aspects of Using Medical Image Data
  • 3.1 Introduction
  • 3.2 Ethical and Privacy Aspects for Data Access
  • 3.2.1 Review by the Medical Ethics Committee
  • 3.2.2 Handling of Informed Consent Procedures
  • 3.2.3 Anonymization
  • 3.2.4 Data Distribution During and After the Benchmarks
  • 3.3 Relevant Legislation
  • 3.4 Procedures Implemented by Data Providers
  • 3.4.1 Agencia D'Informació, Avaluació i Qualitat en Salut, Spain
  • 3.4.2 Medizinische Universität Wien (Austria)
  • 3.4.3 Universitätsklinikum Heidelberg (Germany).
  • 3.5 Aspects, Recommendations and Conditions for Obtaining Approval from Ethical Committees
  • 3.6 Conclusion
  • References
  • 4 Annotating Medical Image Data
  • 4.1 Introduction
  • 4.2 3D Annotation Software
  • 4.2.1 Evaluation Criteria
  • 4.2.2 Reviewed Annotation Tools
  • 4.2.3 Tool Comparison
  • 4.2.4 Selected Software and Technical Aspects
  • 4.3 VISCERAL Ticketing Tool/Framework
  • 4.3.1 Ticketing System Database
  • 4.3.2 Annotation Ticket Life Cycle
  • 4.3.3 Manual Annotation Instructions
  • 4.4 Inter-annotator Agreement
  • 4.5 Conclusion
  • References
  • 5 Datasets Created in VISCERAL
  • 5.1 Introduction
  • 5.2 Anatomy Gold Corpus
  • 5.3 Anatomy Silver Corpus
  • 5.4 Detection Gold Corpus
  • 5.5 Retrieval Gold Corpus
  • 5.6 Retrieval Silver Corpus
  • 5.7 Summary
  • References
  • Part III VISCERAL Benchmarks
  • 6 Evaluation Metrics for Medical Organ Segmentation and Lesion Detection
  • 6.1 Introduction
  • 6.2 Metrics for VISCERAL Benchmarks
  • 6.2.1 Metrics for Segmentation
  • 6.2.2 Metrics for Lesion Detection
  • 6.3 Analysis of Fuzzy Segmentation Metrics
  • 6.3.1 Metric Sensitivity Against Fuzzification
  • 6.3.2 Ranking Systems Using Binary/Fuzzy Ground Truth
  • 6.4 Analysis of Metrics Using Manual Rankings
  • 6.4.1 Dataset
  • 6.4.2 Manual Versus Metric Rankings at Segmentation Level
  • 6.4.3 Manual Versus Metric Rankings at System Level
  • 6.4.4 Discussion of the Manual Ranking Analysis
  • 6.5 Conclusion
  • References
  • 7 VISCERAL Anatomy Benchmarks for Organ Segmentation and Landmark Localization: Tasks and Results
  • 7.1 Introduction
  • 7.2 Data and Data Format
  • 7.2.1 Data
  • 7.2.2 Gold Corpus: Training Set
  • 7.2.3 Gold Corpus: Test Set
  • 7.2.4 Data Format
  • 7.3 Tasks
  • 7.4 Results
  • 7.4.1 Anatomy1
  • 7.4.2 Anatomy2: Intermediate Results at the ISBI Challenge
  • 7.4.3 Anatomy2: Main Benchmark
  • 7.4.4 Anatomy3
  • 7.4.5 Discussion.
  • 7.5 Conclusion
  • References
  • 8 Retrieval of Medical Cases for Diagnostic Decisions: VISCERAL Retrieval Benchmark
  • 8.1 Introduction
  • 8.2 Dataset
  • 8.3 Medical Case-Based Retrieval
  • 8.4 Evaluation
  • 8.4.1 Relevance Judgements
  • 8.4.2 Metrics
  • 8.5 Participants
  • 8.6 Results
  • 8.7 Conclusion
  • References
  • Part IV VISCERAL Anatomy Participant Reports
  • 9 Automatic Atlas-Free Multiorgan Segmentation of Contrast-Enhanced CT Scans
  • 9.1 Introduction
  • 9.2 Method
  • 9.2.1 Process 1: Scan-Specific Characterization
  • 9.2.2 Process 2: Generic Four-Step Segmentation
  • 9.2.3 Process 2: Implementation details
  • 9.2.4 Post-processing at the End of Process 2
  • 9.3 The VISCERAL Benchmark
  • 9.4 Results and Discussion
  • 9.5 VISCERAL Benchmark Perspective
  • 9.6 Conclusion
  • References
  • 10 Multiorgan Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information
  • 10.1 Introduction
  • 10.2 Statistical Shape-Prior-Guided Level Set Segmentation
  • 10.3 Multiorgan Segmentation Using Hierarchical Shape Priors
  • 10.3.1 Building Hierarchical Shape Priors
  • 10.3.2 Multiorgan Segmentation Using Hierarchical Shape Priors
  • 10.3.3 Region-Based External Speed Function
  • 10.4 Improving Segmentation Accuracy Using Model-Guided Local Phase Analysis
  • 10.4.1 Quadrature Filters and Model-Guided Local Phase Analysis
  • 10.4.2 Integrating Region-Based and Edge-Based Energy in the Level Set Method
  • 10.5 Speeding up Level Set Segmentation Using Coherent Propagation
  • 10.6 Experiments and Results
  • 10.7 Discussion and Conclusion
  • References
  • 11 Automatic Multiorgan Segmentation Using Hierarchically Registered Probabilistic Atlases
  • 11.1 Introduction and Related Work
  • 11.2 Methods
  • 11.2.1 SURF Keypoint-Based Image Registration
  • 11.2.2 Organ Atlas Construction.
  • 11.2.3 Image Clustering
  • 11.2.4 Multiorgan Image Segmentation
  • 11.3 Evaluation Results and Discussion
  • 11.4 Concluding Remarks and Future Work
  • References
  • 12 Multiatlas Segmentation Using Robust Feature-Based Registration
  • 12.1 Introduction
  • 12.1.1 Related Work
  • 12.1.2 Our Approach
  • 12.2 Methods
  • 12.2.1 Pairwise Registration
  • 12.2.2 Label Fusion with a Random Forest Classifier
  • 12.2.3 Graph Cut Segmentation with a Potts Model
  • 12.3 Experimental Evaluation
  • 12.3.1 Challenge Results
  • 12.3.2 Detailed Evaluation
  • 12.4 Conclusions
  • References
  • Part V VISCERAL Retrieval Participant Reports
  • 13 Combining Radiology Images and Clinical Metadata for Multimodal Medical Case-Based Retrieval
  • 13.1 Introduction
  • 13.2 Materials and Methods
  • 13.2.1 Dataset
  • 13.2.2 VISCERAL Retrieval Benchmark Evaluation Setup
  • 13.2.3 Multimodal Medical Case Retrieval
  • 13.3 Results
  • 13.3.1 Lessons Learned
  • 13.4 Conclusions
  • References
  • 14 Text- and Content-Based Medical Image Retrieval in the VISCERAL Retrieval Benchmark
  • 14.1 Introduction
  • 14.2 Methods
  • 14.2.1 Term Weighting Retrieval
  • 14.2.2 Semantics Retrieval
  • 14.2.3 BoVW Retrieval
  • 14.2.4 Retrieval Result Refinement
  • 14.2.5 Fusion Retrieval
  • 14.3 Results and Discussion
  • 14.4 Conclusion
  • References
  • Index.

Similar Items