TY  - BOOK
AU  - Zhou,Luping
AU  - Sarikaya,Duygu
AU  - Kia,Seyed Mostafa
AU  - Speidel,Stefanie
AU  - Malpani,Anand
AU  - Hashimoto,Daniel
AU  - Habes,Mohamad
AU  - Löfstedt,Tommy
AU  - Ritter,Kerstin
AU  - Wang,Hongzhi
ED  - OR 2.0 (Workshop)
ED  - MLCN (Workshop)
ED  - International Conference on Medical Image Computing and Computer-Assisted Intervention
TI  - OR 2.0 context-aware operating theaters and machine learning in clinical Neuroimaging: second International Workshop, OR 2.0 2019, and second International Workshop, MLCN 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings 
T2  - Lecture notes in computer science
SN  - 9783030326951
AV  - RD29.7 
U1  - 617.00285 23
PY  - 2019///
CY  - Cham, Switzerland
PB  - Springer
KW  - Computer-assisted surgery
KW  - Congresses
KW  - Surgical robots
KW  - Diagnostic imaging
KW  - Digital techniques
KW  - Chirurgie assistée par ordinateur
KW  - Congrès
KW  - Robots chirurgiens
KW  - Imagerie pour le diagnostic
KW  - Techniques numériques
KW  - fast
KW  - Congress
KW  - proceedings (reports)
KW  - aat
KW  - Conference papers and proceedings
KW  - lcgft
KW  - Actes de congrès
KW  - rvmgf
N1  - International conference proceedings; Includes author index; Intro; Additional Workshop Editors; OR 2.0 2019 Preface; OR 2.0 2019 Organization; MLCN 2019 Preface; MLCN 2019 Organization; Contents; Proceedings of the 2nd International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019); Feature Aggregation Decoder for Segmenting Laparoscopic Scenes; 1 Introduction; 2 Method; 2.1 Xception Encoder; 2.2 Feature Aggregation Decoder; 3 Experimental Results and Discussions; 4 Conclusions; References; Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories; 1 Introduction; 2 Materials and Methods; 3 Experiments4 Results; 5 Discussion and Conclusion; References; Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke; 1 Introduction; 2 Method; 2.1 Guided-Unsharp Upsample Loss; 2.2 Aggregate Loss Function; 2.3 Atrous Convolution Feature Extraction Module; 2.4 Generator and Discriminator Networks; 3 Experimentation and Results; 3.1 Dataset and Implementation Details; 3.2 Results; 4 Conclusion; References; Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration; 1 Introduction; 2 Methods; 2.1 RSD Model; 2.2 Unsupervised Temporal Video Segmentation Model2.3 Combined Learning Pipelines; 2.4 Corridor-Based RSD Loss Function; 3 Evaluation; 3.1 Baselines; 3.2 Results; 4 Conclusion; References; Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis; 1 Introduction; 2 Methods; 2.1 Multispectral Imaging Hardware; 2.2 End-to-End Deep Learning Pipeline for Multispectral Image Analysis; 3 Experiments and Results; 3.1 In Silico Quantitative Validation; 3.2 In Vivo Qualitative Validation; 4 Discussion; References; Towards a Cyber-Physical Systems Based Operating Room of the Future; 1 Introduction2 Methods; 2.1 Intelligent Surgical Theatre Architecture; 2.2 Cyber-Twin; 2.3 Cognitive Engine and Machine Learning; 3 RadioFrequency Ablation Needle Insertion Robot; 4 Discussion and Conclusion; References; Proceedings of the 2nd International Workshop on Machine Learning in Clinical Neuroimaging: Entering the Era of Big Data via Transfer Learning and Data Harmonization (MLCN 2019); Deep Transfer Learning for Whole-Brain FMRI Analyses; 1 Introduction; 2 Methods; 2.1 Data; 2.2 DeepLight; 3 Results; 3.1 Pre-training Data; 3.2 Test Data; 4 Conclusion; References; Knowledge Distillation for Semi-supervised Domain Adaptation1 Introduction; 2 Related Work; 3 Methods; 3.1 Knowledge Distillation for Domain Adaptation; 4 Experiments and Results; 4.1 Databases; 4.2 Experimental Setup; 4.3 Results; 5 Discussion; References; Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors; 1 Introduction; 2 Data; 2.1 Data Pre-processing and Feature Extraction; 3 The Relevance Vector Machine for Data Harmonization; 4 Results; 4.1 Verification of Observable Correlations in Data; 4.2 Harmonization of Healthy Population Data Based on RVM
N2  - This book constitutes the refereed proceedings of the Second International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For OR 2.0 all 6 submissions were accepted for publication. They aim to highlight the potential use of machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors, wearable and implantable electronics and robots, visual attention models, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating theater, human-robot collaborative systems, and surgical training and assessment. MLCN 2019 accepted 6 papers out of 7 submissions for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience
UR  - https://link.springer.com/10.1007/978-3-030-32695-1
ER  -