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• Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis.- 3D Brain MRI GAN-based Synthesis Conditioned on Partial Volume Maps.- Synthesizing Realistic Brain MR Images With Noise Control.- Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth.- Blind MRI Brain Lesion Inpainting Using Deep Learning.- High-Quality Interpolation of Breast DCE-MRI Using Learned Transformations.- A Method for Tumor Treating Fields Fast Estimation.- Heterogeneous Virtual Population of Simulated CMR Images for Improving the Generalization of Cardiac Segmentation Algorithms.- DyeFreeNet: Deep Virtual Contrast CT Synthesis.- A Gaussian Process Model Based Generative Framework for Data Augmentation of Multi-modal 3D Image Volumes.- Frequency-selective Learning for CT to MR Synthesis.- Uncertainty-aware Multi-resolution Whole-body MR to CT Synthesis.- UltraGAN: Ultrasound Enhancement Through Adversarial Generation.- Improving Endoscopic Decision Support Systems by Translating Between Imaging Modalities.- An Unsupervised Adversarial Learning Approach to Fundus Fluorescein Angiography Image Synthesis for Leakage Detection.- Towards Automatic Embryo Staging in 3D+t Microscopy Images Using Convolutional Neural Networks and PointNets.- Train Small, Generate Big: Synthesis of Colorectal Cancer Histology Images.- Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis.- Auditory Nerve Fiber Health Estimation Using Patient Specific Cochlear Implant Stimulation Models.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement or segmentation.
(source: Nielsen Book Data)

• Chapter 1. Data Analytics: COVID-19 Prediction using Multimodal Data.-
• Chapter 2. COVID-19 Apps: Privacy and security concerns.-
• Chapter 3. Coronavirus Outbreak: Multi-objective Prediction and Optimization.-
• Chapter 4. AI-Enabled Framework to Prevent COVID-19 from Further Spreading.-
• Chapter 5. Artificial Intelligence Enabled Robotic Drones for COVID-19 Outbreak.-
• Chapter 6. Understanding and Analysis of Enhanced COVID-19 Chest X-Ray Images.-
• Chapter 7. Deep Learning-based COVID-19 Diagnosis and Trend Predictions.-
• Chapter 8. COVID-19: Loose Ends.-
• Chapter 9. Social Distancing and Artificial Intelligence- Understanding the Duality in the times of Covid-19.-
• Chapter 10. Post Covid-19 and Business Analytics.
• (source: Nielsen Book Data)

This book discusses intelligent systems and methods to prevent further spread of COVID-19, including artificial intelligence, machine learning, computer vision, signal processing, pattern recognition, and robotics. It not only explores detection/screening of COVID-19 positive cases using one type of data, such as radiological imaging data, but also examines how data analytics-based tools can help predict/project future pandemics. In addition, it highlights various challenges and opportunities, like social distancing, and addresses issues such as data collection, privacy, and security, which affect the robustness of AI-driven tools. Also investigating data-analytics-based tools for projections using time series data, pattern analysis tools for unusual pattern discovery (anomaly detection) in image data, as well as AI-enabled robotics and its possible uses, the book will appeal to a broad readership, including academics, researchers and industry professionals.
(source: Nielsen Book Data)

• Image Segmentation.- Image Registration, Reconstruction and Enhancement.- Radiomics, Predictive Models, and Quantitative Imaging Biomarkers.- Ocular Imaging Analysis.- Biomedical Simulation and Modelling.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.
(source: Nielsen Book Data)

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Brain Lesion Image Analysis
• Brain Tumor Image Segmentation
• Combined MRI and Pathology Brain Tumor Classification
• Tools Allowing Clinical Translation of Image Computing Algorithms.

The two-volume set LNCS 11992 and 11993 constitutes the thoroughly refereed proceedings of the 5th International MICCAI Brainlesion Workshop, BrainLes 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algorithms (TACTICAL). These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI, in Shenzhen, China, in October 2019. The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (12 selected papers from 32 submissions); brain tumor image segmentation (58 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).

• Part I: Biomedical Engineering, Trends, Research and Technologies
• Vision Paper for Enabling Internet of Medical Robotics Things in Open Healthcare Platform 2030
• Stumbling Blocks of Utilizing Medical and Health Data: Success Factors Extracted from Australia-Japan Comparison
• Digital Financial Incentives for Improved Population Health in the Americas
• Part II: Advanced ICT for Medicine and Healthcare
• Trial Run of a Patient Call System using Mobile Devices
• Advance Watermarking Algorithm using SURF with DWT and DCT for CT Images
• Improving Depth Perception using Multiple Iso-Surfaces for Transparent Stereoscopic Visualization of Medical Volume Data
• Design and Simulation of a Robotic Manipulator for Laparoscopic Uterine Surgeries
• Self-Skill Training System for Chest Compressions in Neonatal Resuscitation Workshop
• Part III. Statistical Signal Processing and Artificial Intelligence
• Comparative Study of Pattern Recognition Methods for Predicting Glaucoma Diagnosis
• Research on Encrypted Face Recognition Algorithm Based on New Combined Chaotic Map and Neural Network
• A 3D Shrinking-and-Expanding Module with Channel Attention for Efficient Deep Learning-Based Super-Resolution
• Dynamic Facial Features in Positive-Emotional Speech for Identification of Depressive Tendencies
• Hand-Crafted and Deep Learning-Based Radiomics Models for Recurrence Prediction of Non-Small Cells Lung Cancers
• Weakly and Semi-supervised Deep Level Set Network for Automated Skin Lesion Segmentation
• Part IV. Support System for Medicine and Healthcare
• A Transcriptional Study of Oncogenes and Tumor Suppressors Altered by Copy Number Variations in Ovarian Cancer
• Analysis of Acoustic Features Affected by Residual Food in the Piriform Fossa Toward Early-Detection of Dysphagia
• Automatic Joint Space Distance Measurement Method for Rheumatoid Arthritis Medical Examinations
• Development of an Active Compression System for Venous Disease
• Design and Development of a Droplet-Based Microfluidics System Using Laser Fabrication Machining Techniques for a Lab on a Chip Device
• Design of a Novel MEMS-Based Microgripper with Hybrid Actuation to Determine Circulating Tumor Cell (CTC) Progression.

This book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-19), held in Split, Croatia, on June 17-19, 2020. Covering a number of key areas, including digital IT architecture in healthcare; advanced ICT for medicine and healthcare; biomedical engineering, trends, research and technologies; and healthcare support systems, this book is a valuable resource for researchers, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields. .
(source: Nielsen Book Data)

• Sensor data synchronization in a IoT environment for infants motricity measurement.- A Real-time Algorithm for PPG Signal Processing During Intense Physical Activity.- Design and Testing of a Textile EMG Sensor for Prosthetic Control.- Design of a smart mechatronic system to combine garments for blind people: first insights IoT for Health applications and solutions.- Towards a smartwatch for cu-less blood pressure measurement using PPG signal and physiological features.- WiFi-enabled Automatic Eating Moment Monitoring Using Smartphones.- SocialBike: Quantified-self Data as Social Cue in Physical Activity.- Assisting Radiologists in X-Ray Diagnostics Design and Evaluation for Digital Forensic Ready Wireless Medical Systems.- An IoT-based Healthcare Ecosystem for Home Intelligent Assistant Services in Smart Homes.
• .
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 6th International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2019, held in Braga, Portugal, in December 2019. The IoT as a set of existing and emerging technologies, notions and services can provide many solutions to delivery of electronic healthcare, patient care, and medical data management. The 10 revised full papers presented were carefully reviewed and selected from 26 submissions. The papers cover topics such as healthcare information systems, consumer health, health informatics, engineering, telecommunications, mathematics and statistics, life and medical sciences, and cloud computing.
(source: Nielsen Book Data)

• Oncology and Tumour Imaging
• Lesion, Wound and Ulcer Analysis
• Biostatistics
• Fetal Imaging
• Enhancement and Reconstruction
• Diagnosis, Classication and Treatment
• Vessel and Nerve Analysis
• Image Registration
• Image Segmentation
• Ophthalmic Imaging
• Posters.

This book constitutes the refereed proceedings of the 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, held in Liverpool, UK, in July 2019. The 43 full papers presented were carefully reviewed and selected from 70 submissions. There were organized in topical sections named: oncology and tumour imaging; lesion, wound and ulcer analysis; biostatistics; fetal imaging; enhancement and reconstruction; diagnosis, classification and treatment; vessel and nerve analysis; image registration; image segmentation; ophthalmic imaging; and posters.

This book constitutes the refereed proceedings of the 16th Conference on Artificial Intelligence in Medicine, AIME 2017, held in Vienna, Austria, in June 2017. The 21 revised full and 23 short papers presented were carefully reviewed and selected from 113 submissions. The papers are organized in the following topical sections: ontologies and knowledge representation; Bayesian methods; temporal methods; natural language processing; health care processes; and machine learning, and a section with demo papers. .
(source: Nielsen Book Data)

• Brain Lesion.- Brain Tumor Segmentation (BRATS).- Ischemic Stroke Lesion Image Segmentation (ISLES), Mild Traumatic Brain Injury Outcome Prediction (mTOP).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 26 papers presented in this volume were carefully reviewed. They present the latest advances in segmentation, disease prognosis and other applications to the clinical context.
(source: Nielsen Book Data)

• State-of-the-art techniques.- Novel and emerging analysis and visualization techniques.- Clinical challenges and open problems.- Major aspects of problems related to spine imaging.- Including clinical applications of spine imaging.- Computer aided diagnosis of spine conditions.- Computer aided detection of spine-related diseases.- Emerging computational imaging techniques for spinal diseases, .-Fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging.- Image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging.- Novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 4th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 13 workshop papers were carefully reviewed and selected for inclusion in this volume. They aim at reviewing the state-of-the-art techniques, sharing the novel and emerging analysis and visualization techniques and discussing the clinical challenges and open problems in this rapidly growing field - including all major aspects of problems related to spine imaging, including clinical applications of spine imaging, computer aided diagnosis of spine conditions, computer aided detection of spine-related diseases, emerging computational imaging techniques for spinal diseases, fast 3D reconstruction of spine, feature extraction, multiscale analysis, pattern recognition, image enhancement of spine imaging, image-guided spine intervention and treatment, multimodal image registration and fusion for spine imaging, novel visualization techniques, segmentation techniques for spine imaging, statistical and geometric modeling for spine and vertebra, spine and vertebra localization.
(source: Nielsen Book Data)

• Ecare@Home: A Distributed Research Environment on Semantic Interoperability
• Halmstad Intelligent Home
• Capabilities and Opportunities
• Healthcare needs, company innovations, and research enabling solutions within Embedded Sensor Systems for Health
• A Case-Based Classification for Drivers? Alcohol Detection Using Physiological Signals
• Towards a Probabilistic Method for Longitudinal Monitoring in Health Care
• A Classification Model for Predicting Heart Failure in Cardiac Patients by Using Unstructured Data
• Ins and Outs of Big Data: A Review
• A review of Parkinson?s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
• Why Hackers Love eHealth Applications
• Reliable Communication in Health Monitoring Applications11 Security Context Framework for Distributed Healthcare IoT Platform
• BitRun: Gamification of Health Data from Fitbit Activity Trackers
• Smartphone-based Decision Support System for Elimination of Pathology-free Images in Diabetic Retinopathy Screening
• Improving Awareness in Ambient-Assisted Living Systems: Consolidated Data Stream Processing
• Beyond?Happy Apps?: Using the Internet of Things to Support Emotional Health
• TEEM: a Mobile App for Technology-Enhanced Emergency Management
• Perception of Delay in Computer Input Devices Establishing a Baseline for Signal Processing of Motion Sensor Systems
• Telemetry system for diagnosis of Asthma and Chronical Obstructive Pulmonary Disease (COPD)
• Remotely Supporting Patients with Obstructive Sleep Apnea at Home
• An aggregation platform for IoT-based healthcare: illustration for bioimpedancemetry, temperature and fatigue level monitoring.

This book constitutes the proceedings of the Third International Conference on Internet of Things (IoT) Technologies for HealthCare, HealthyIoT 2016, held in Västerås, Sweden, October 18-19, 2016. The conference also included the First Workshop on Emerging eHealth through Internet of Things (EHIoT 2016). IoT as a set of existing and emerging technologies, notions and services provides many solutions to delivery of electronic healthcare, patient care, and medical data management. The 31 revised full papers presented along with 9 short papers were carefully reviewed and selected from 43 submissions in total. The papers cover topics such as healthcare support for the elderly, real-time monitoring systems, security, safety and communication, smart homes and smart caring environments, intelligent data processing and predictive algorithms in e-Health, emerging eHealth IoT applications, signal processing and analysis, and smartphones as a healthy thing.

• Spectral methods
• Longitudinal methods
• Shape methods.

This book constitutes the refereed post-conference proceedings of the First International Workshop on Spectral and Shape Analysis in Medical Imaging, SeSAMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 10 submitted full papers presented in this volume were carefully reviewed. The papers reflect the following topics: spectral methods; longitudinal methods; and shape methods.

• Active learning
• Semi-supervised learning
• Reinforcement learning
• Domain adaptation and transfer learning
• Crowd-sourcing annotations and fusion of labels from different sources
• Data augmentation
• Modelling of label uncertainty
• Visualization and human-computer interaction
• Image description
• Medical imaging-based diagnosis
• Medical signal-based diagnosis
• Medical image reconstruction and model selection using deep learning techniques
• Meta-heuristic techniques for fine-tuning
• Parameter in deep learning-based architectures
• Applications based on deep learning techniques.

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty. The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

• Medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain
• Data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues
• Computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.

This book constitutes the refereed proceedings of the 5th International Conference on Health Information Science, HIS 2016, held in Shanghai, China, in November 2016. The 13 full papers and 9 short papers presented were carefully reviewed and selected from numerous submissions. The scope of the papers includes medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer aided diagnosis; development of new architectures and applications for health information systems.

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

• Brain analysis
• Brain analysis
• connectivity
• Brain analysis
• cortical morphology
• Alzheimer disease
• Surgical guidance and tracking
• Computer aided interventions
• Ultrasound image analysis
• cancer image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis; brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Machine learning and feature selection.- Deep learning in medical imaging.- Applications of machine learning.- Segmentation.- Cell image analysis.
• (source: Nielsen Book Data)

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.
(source: Nielsen Book Data)

• Registration and deformation estimation
• Shape modeling
• Cardiac and vascular image analysis
• Image reconstruction
• MR image analysis.

The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis, brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

• Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning
• Construction of Neonatal Diffusion Atlases via Spatio-Angular Consistency
• Selective Labeling: identifying representative sub-volumes for interactive segmentation
• Robust and Accurate Appearance Models based on Joint Dictionary Learning: Data from the Osteoarthritis Initiative
• Consistent multi-atlas hippocampus segmentation for longitudinal MR brain images with temporal sparse representation
• Sparse-Based Morphometry: Principle and Application to Alzheimer's Disease
• Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning
• Patch-Based Discrete Registration of Clinical Brain Images
• Non-local MRI Library-based Super-resolution: Application to Hippocampus Subfield Segmentation
• Patch-based DTI grading: Application to Alzheimer's disease classification
• Hierarchical Multi-Atlas Segmentation using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement
• HIST: HyperIntensity Segmentation Tool
• Supervoxel-Based Hierarchical Markov Random Field Framework for Multi-Atlas Segmentation
• CapAIBL: Automated reporting of cortical PET quantification without need of MRI on brain surface using a patch-based method
• High resolution hippocampus subfield segmentation using multispectral multi-atlas patch-based label fusion
• Identification of water and fat images in Dixon MRI using aggregated patch-based convolutional neural networks
• Estimating Lung Respiratory Motion Using Combined Global and Local Statistical Models.

This book constitutes the refereed proceedings of the Second International Workshop on Patch-Based Techniques in Medical Images, Patch-MI 2016, which was held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016. The 17 regular papers presented in this volume were carefully reviewed and selected from 25 submissions. The main aim of the Patch-MI 2016 workshop is to promote methodological advances within the medical imaging field, with various applications in image segmentation, image denoising, image super-resolution, computer-aided diagnosis, image registration, abnormality detection, and image synthesis.