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

Description

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. Front Matter ....Pages i-xvi Front Matter ....Pages 1-1 Feature Aggregation Decoder for Segmenting Laparoscopic Scenes (Abdolrahim Kadkhodamohammadi, Imanol Luengo, Santiago Barbarisi, Hinde Taleb, Evangello Flouty, Danail Stoyanov)....Pages 3-11 Preoperative Planning for Guidewires Employing Shape-Regularized Segmentation and Optimized Trajectories (Johannes Fauser, Moritz Fuchs, Ahmed Ghazy, Bernhard Dorweiler, Anirban Mukhopadhyay)....Pages 12-20 Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke (V. Vishal, Neeraj Sharma, Munendra Singh)....Pages 21-28 Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration (Dominik Rivoir, Sebastian Bodenstedt, Felix von Bechtolsheim, Marius Distler, Jürgen Weitz, Stefanie Speidel)....Pages 29-37 Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis (Leonardo A. Ayala, Sebastian J. Wirkert, Janek Gröhl, Mildred A. Herrera, Adrian Hernandez-Aguilera, Anant Vemuri et al.)....Pages 38-46 Towards a Cyber-Physical Systems Based Operating Room of the Future (Chin-Boon Chng, Pooi-Mun Wong, Nicholas Ho, Xiaoyu Tan, Chee-Kong Chui)....Pages 47-55 Front Matter ....Pages 57-57 Deep Transfer Learning for Whole-Brain FMRI Analyses (Armin W. Thomas, Klaus-Robert Müller, Wojciech Samek)....Pages 59-67 Knowledge Distillation for Semi-supervised Domain Adaptation (Mauricio Orbes-Arteainst, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat et al.)....Pages 68-76 Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors (Maria Ines Meyer, Ezequiel de la Rosa, Koen Van Leemput, Diana M. Sima)....Pages 77-85 Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation (Annika Hänsch, Bastian Cheng, Benedikt Frey, Carola Mayer, Marvin Petersen, Iris Lettow et al.)....Pages 86-94 A Hybrid 3DCNN and 3DC-LSTM Based Model for 4D Spatio-Temporal fMRI Data: An ABIDE Autism Classification Study (Ahmed El-Gazzar, Mirjam Quaak, Leonardo Cerliani, Peter Bloem, Guido van Wingen, Rajat Mani Thomas)....Pages 95-102 Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites (Florian Dubost, Max Dünnwald, Denver Huff, Vincent Scheumann, Frank Schreiber, Meike Vernooij et al.)....Pages 103-111 Back Matter ....Pages 113-114