Patch-Based Techniques in Medical Imaging: First International Workshop, Patch-MI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015, Revised Selected Papers

✍ Scribed by Guorong Wu, Pierrick Coupé, Yiqiang Zhan, Brent Munsell, Daniel Rueckert (eds.)

• Publisher: Springer International Publishing
• Year: 2015
• Tongue: English
• Leaves: 225
• Series: Lecture Notes in Computer Science 9467
• Edition: 1
• Category: Library

✦ Synopsis

This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015.

The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data.

✦ Table of Contents

Front Matter....Pages I-IX
A Multi-level Canonical Correlation Analysis Scheme for Standard-Dose PET Image Estimation....Pages 1-9
Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image....Pages 10-18
Automatic Hippocampus Labeling Using the Hierarchy of Sub-region Random Forests....Pages 19-27
Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis....Pages 28-36
Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-High Field MRI....Pages 37-45
Automatic Liver Tumor Segmentation in Follow-Up CT Scans: Preliminary Method and Results....Pages 46-53
Block-Based Statistics for Robust Non-parametric Morphometry....Pages 54-61
Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-Based Edge Detection....Pages 62-70
Efficient Lung Cancer Cell Detection with Deep Convolution Neural Network....Pages 71-78
An Effective Approach for Robust Lung Cancer Cell Detection....Pages 79-86
Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation....Pages 87-94
Hippocampus Segmentation Through Distance Field Fusion....Pages 95-103
Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing....Pages 104-111
Fast Regions-of-Interest Detection in Whole Slide Histopathology Images....Pages 112-119
Reliability Guided Forward and Backward Patch-Based Method for Multi-atlas Segmentation....Pages 120-127
Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptors....Pages 128-136
Multi-atlas Segmentation Using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression....Pages 137-145
A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images....Pages 146-153
3D MRI Denoising Using Rough Set Theory and Kernel Embedding Method....Pages 154-162
A Novel Cell Orientation Congruence Descriptor for Superpixel Based Epithelium Segmentation in Endometrial Histology Images....Pages 163-171
Patch-Based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques....Pages 172-179
Multi-atlas and Multi-modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph....Pages 180-187
Prediction of Infant MRI Appearance and Anatomical Structure Evolution Using Sparse Patch-Based Metamorphosis Learning Framework....Pages 188-196
Efficient Multi-scale Patch-Based Segmentation....Pages 197-204
Back Matter....Pages 205-213
....Pages 215-216

✦ Subjects

Image Processing and Computer Vision; Pattern Recognition; Computer Graphics; Artificial Intelligence (incl. Robotics); Simulation and Modeling; Algorithm Analysis and Problem Complexity