Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures

Name: Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures | 4th International Workshop, CARE 2017, and 6th International Workshop, CLIP 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings
Brand: Springer
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4th International Workshop, CARE 2017, and 6th International Workshop, CLIP 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings

M. Jorge Cardoso Tal Arbel Xiongbiao Luo Stefan Wesarg Tobias Reichl Miguel Ángel González Ballester Jonathan McLeod Klaus Drechsler Terry Peters Marius Erdt Kensaku Mori Marius George Linguraru Andreas Uhl Cristina Oyarzun Laura Raj Shekhar(Editor)

Springer (Publisher)

Published on 6. September 2017

XIV, 182 pages

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978-3-319-67543-5 (ISBN)

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Content

Intro
Workshop Editors
Preface CARE 2017
Organization
Preface CLIP 2017
Organization
Contents
4th International Workshop on Computer Assisted and Robotic Endoscopy, CARE 2017
Shape-Based Pose Estimation of Robotic Surgical Instruments
1 Introduction
2 Related Work
3 Shape-Based Pose Estimation
3.1 Detection
3.2 Learning
4 Experiments and Results
5 Conclusion
References
3D Endoscope System Using Asynchronously Blinking Grid Pattern Projection for HDR Image Synthesis
1 Introduction
2 Related Work
3 DOE-Based Laser Pattern Projector for Endoscopy
3.1 System Configuration
3.2 3D Reconstruction
4 Auto-Calibration of the Projector Position
5 HDR Synthesis Using Asynchronous Blinking Pattern
6 Experiments
6.1 Improvement Using HDR Image for 3D Reconstruction
6.2 3D Reconstruction Inside Stomach of a Pig
7 Conclusion
References
Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis
1 Introduction
2 Method
2.1 Reference Real-Time Still Frame-Based Polyp Detection Method
2.2 Combination of Feature Types
2.3 Spatio-Temporal Coherence Module
3 Experimental Setup
3.1 Validation Database
3.2 Performance Metrics
4 Results
4.1 Quantitative Results
5 Discussion
5.1 Impact of Adaptation Strategy on Method's Performance
5.2 Frame-Based Analysis vs. Clinical Applicability
5.3 Analysis of Methods' Performance in the Context of the State-of-the-Art
6 Conclusions
References
Progressive Hand-Eye Calibration for Laparoscopic Surgery Navigation
Abstract
1 Introduction
2 Methods
2.1 Hardware
2.2 Progressive Hand-Eye Calibration
3 Experiments
4 Results
5 Conclusion
Acknowledgments
References
Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN)
Abstract
1 Introduction
2 Method
2.1 Pre-processing and SfM
2.2 Model for Estimating Camera Pose
3 Dataset
3.1 Simulated Video
3.2 Real Colonoscopy Video
4 Experiments and Results
4.1 Simulated Video
4.2 Validation Using a Colonoscopy Phantom
4.3 Application to Actual Colonoscopy Video
5 Discussion and Conclusion
References
Motion Vector for Outlier Elimination in Feature Matching and Its Application in SLAM Based Laparoscopic Tracking
1 Introduction
2 Methodology
2.1 MV-Based Method for Motion Estimation
2.2 Application in ORB-SLAM Based Tracking
3 Experiments and Results
3.1 Detection Rate in Image Pairs
3.2 Performance in Laparoscopic Video
4 Discussion
5 Conclusion and Future Work
References
Image-Based Smoke Detection in Laparoscopic Videos
1 Introduction
2 Related Work
3 Proposed Methodologies
3.1 Saturation Peak Analysis (SPA)
3.2 CNN Classification
4 Experimental Results
4.1 Datasets
4.2 Evaluation Results - DS A
4.3 Evaluation Results - DS B
4.4 Runtime Evaluation
4.5 Discussion
5 Conclusion
References
6th International Workshop on Clinical Image-Based Procedures, CLIP 2017
Fully Automatic Detection of Distal Radius Fractures from Posteroanterior and Lateral Radiographs
1 Introduction
2 Background
3 Method
3.1 Modeling and Matching
3.2 Classification
4 Experiments
5 Conclusions
References
Automated Characterization of Pyelocalyceal Anatomy Using CT Urograms to Aid in Management of Kidne ...
Abstract
1 Introduction
2 Methods
2.1 Patient Selection and Imaging
2.2 Automated Localization and Segmentation of Whole Kidney
2.3 Automated Segmentation of Pyelocalyceal Anatomy and Validation
2.4 Measurement of Infundibulopelvic Angle in 2D and 3D Images
3 Results
3.1 Patients
3.2 Pyelocalyceal Anatomy Segmentation
3.3 Infundibulopelvic Angle
4 Discussion
Acknowledgements
References
Local Phase-Based Learning for Needle Detection and Localization in 3D Ultrasound
1 Introduction
2 Methods
2.1 Needle Detection
2.2 Needle Enhancement
2.3 Tip Localization
2.4 Data Acquisition and Experimental Validation
3 Results
4 Discussion and Conclusions
References
Intracranial Volume Quantification from 3D Photography
Abstract
1 Introduction
2 Materials and Methods
2.1 Data Description
2.2 Intracranial Volume Quantification from CT
2.3 Head Volume Quantification from 3D Photography
2.4 Intracranial Volume Estimation from 3D Photography
3 Evaluation and Results
4 Conclusions
Acknowledgements
References
Automatic Near Real-Time Evaluation of 3D Ultrasound Scan Adequacy for Developmental Dysplasia of the Hip
1 Introduction
2 Methods
2.1 Materials and Experimental Setup
2.2 Ultrasound Scan Adequacy Criteria
2.3 CNN Architecture
2.4 Training
3 Results and Discussion
3.1 Criteria for Validation
4 Conclusions
References
Automatic Sentinel Lymph Node Localization in Head and Neck Cancer Using a Coupled Shape Model Algorithm
1 Introduction
2 Methods
2.1 Lymph Node Localisation
2.2 Spect-CT - CT Registration
2.3 CoSMo Adaptation
2.4 Lymph Node Level Determination
3 Evaluation
4 Conclusion
References
Towards an Automated Segmentation of the Ventro-Intermediate Thalamic Nucleus
Abstract
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Thalamic Parcellation
2.3 The Proposed Framework
2.4 Multi-atlas Segmentation
3 Results
4 Discussion
Acknowledgements
References
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer
1 Introduction
2 Classification of Confocal Patterns
3 Experiments
3.1 Experimental Set-Up
3.2 Results
4 Conclusions
References
Automated Classification for Breast Cancer Histopathology Images: Is Stain Normalization Important?
1 Introduction
2 Proposed Approach
2.1 Dataset Description
2.2 Stain Normalization Procedure
2.3 Feature Descriptors and Contemporary Classifiers
3 Results and Discussion
3.1 Training-Testing Protocol and Evaluation Metric
3.2 Performance Evaluation and Comparison
4 Conclusion
References
Hybrid Tracking for Improved Registration of Laparoscopic Ultrasound and Laparoscopic Video for Augm ...
Abstract
1 Introduction
2 Methods
2.1 System Calibration for AR
2.2 Improved System Calibration for AR
2.3 LUS Probe Model and Calibrations
2.4 Model Projection and Alignment
3 Experiments and Results
4 Discussion and Conclusion
Acknowledgement
References
Author Index

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Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures

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