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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.

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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.

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