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

225 Pages·2015·30.418 MB·English

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

Guorong Wu · Pierrick Coupé
Yiqiang Zhan · Brent Munsell
Daniel Rueckert (Eds.)
7
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Patch-Based Techniques
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First International Workshop, Patch-MI 2015
Held in Conjunction with MICCAI 2015
Munich, Germany, October 9, 2015, Revised Selected Papers
123
Lecture Notes in Computer Science 9467
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é
Guorong Wu Pierrick Coup
(cid:129)
Yiqiang Zhan Brent Munsell
(cid:129)
Daniel Rueckert (Eds.)
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
123
Editors
Guorong Wu BrentMunsell
University of NorthCarolina atChapel Hill Collegeof Charleston
ChapelHill, NC Charleston, SC
USA USA
Pierrick Coupé DanielRueckert
Bordeaux University Imperial CollegeLondon
Bordeaux London
France UK
YiqiangZhan
Siemens Healthcare
Malvern, PA
USA
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-319-28193-3 ISBN978-3-319-28194-0 (eBook)
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Preface
The First International Workshop on Patch-Based Techniques in Medical Imaging
(Patch-MI 2015) was held at Munich, Germany, on October 9, 2015, in conjunction
with the 18th International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI).
The patch-based technique plays an increasing role in the medical imaging field,
with various applications in image segmentation, image denoising, image
super-resolution, computer-aided diagnosis, image registration, abnormality detection,
andimagesynthesis.Forexample,patch-basedapproachesusingthetraininglibraryof
annotated atlases have been the focus of much attention in segmentation and
computer-aided diagnosis. It has been shown that the patch-based strategy based on a
traininglibraryisabletoproduceanaccuraterepresentationofdata,whiletheuseofa
training library enables one to easily integrate prior knowledge to the model. As an
intermediate level between the global image and the localized voxel, patch-based
models offer an efficient and flexible way to represent very complex anatomies.
The main aim of the Patch-MI 2015 workshop was to promote methodological
advanceswithinthefieldofpatch-basedprocessinginmedicalimaging.Theworkshop
focused on major trends and challenges in this area, and tried to identify new
cutting-edge techniques and their use in medical imaging. We hope the workshop
becomes a new platform for translating research from the bench to the bedside. We
lookfororiginal,high-qualitysubmissionsoninnovativeresearch anddevelopmentin
the analysis of medical image data using patch-based techniques.
The quality of submissions for this year’s meeting was very high. Authors were
asked to submit eight-page papers for review. A total of 35 papers were submitted to
the workshop in response to the call for papers. Each of the 35 papers underwent a
rigorous double-blinded peer-review process, with each paper being reviewed by at
least two (typically three) reviewers from the Program Committee, composed of 62
well-known experts in the field. Based on the reviewing scores and critiques, the 25
best papers were accepted for presentation at the workshop and chosen to be included
in this volume of Lecture Notes in Computer Science published by Springer. A large
varietyofpatch-basedtechniquesappliedtomedicalimagingwerewellrepresentedat
the workshop.
We are grateful to the Program Committee for reviewing the submitted papers and
giving constructive comments andcritiques, tothe authors for submitting high-quality
papers,tothepresentersfortheirexcellentpresentations,andtoallthePatch-MI2015
attendees who came to Munich from all around the world.
October 2015 Pierrick Coupé
Guorong Wu
Yiqiang Zhan
Daniel Rueckert
Brent Munsell
Organization
Program Committee
Paul Aljabar King’s College London, UK
Christian Barillot IRISA, France
Jérome Boulanger Curie Institute, France
Weidong Cai The University of Sydney, Australia
Louis Collins McConnell Brain Imaging Centre, McGill, Canada
Olivier Colliot University of Pierre and Marie Curie, France
Olivier Commowick Inria Rennes – Bretagne Atlantique, France
Maxime Descoteaux Centre de Recherche CHUS, Canada
Simon Eskildsen Center of Functionally Integrative Neuroscience,
Denmark
Yong Fan University of Pennsylvania, USA
Vladimir Fonov McGill University, Canada
Hayit Greenspan Tel Aviv University, Israel
Ghassan Hamarneh Simon Fraser University, Canada
Rolf Heckemann Sahlgrenska University Hospital, Sweden
Mattias Heinrich Universität Lübeck, Germany
Junzhou Huang University of Texas at Arlington, USA
Charles Kervrann Inria Rennes – Bretagne Atlantique, France
Tobias Klinder Philips Research Europe, Germany
Karim Lekadir Universitat Pompeu Fabra, Spain
Gang Li University of North Carolina at Chapel Hill, USA
Shu Liao Siemens, USA
Jean-Francois Mangin CEA, France
Jose Manjón ITACA Institute, Universidad Politecnica de Valencia,
Spain
Jerry Prince Johns Hopkins University, USA
François Rousseau Telecom Bretagne, France
Mert Sabuncu Harvard Medical School, USA
Dinggang Shen University of North Carolina at Chapel Hill, USA
Li Shen Indiana University, USA
Yonggang Shi University of South California, USA
Jussi Tohka Universidad Carlos III de Madrid, Spain
Qian Wang Shanghai Jiao Tong University, China
Li Wang University of North Carolina at Chapel Hill, USA
Lin Yang University of Florida, USA
Shaoting Zhang University of North Carolina at Charlotte, USA
Guoyan Zheng University of Bern, Switzerland
Contents
A Multi-level Canonical Correlation Analysis Scheme for Standard-Dose
PET Image Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Le An, Pei Zhang, Ehsan Adeli-Mosabbeb, Yan Wang, Guangkai Ma,
Feng Shi, David S. Lalush, Weili Lin, and Dinggang Shen
Image Super-Resolution by Supervised Adaption of Patchwise
Self-similarity from High-Resolution Image . . . . . . . . . . . . . . . . . . . . . . . . 10
Guorong Wu, Xiaofeng Zhu, Qian Wang, and Dinggang Shen
Automatic Hippocampus Labeling Using the Hierarchy of Sub-region
Random Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Lichi Zhang, Qian Wang, Yaozong Gao, Guorong Wu,
and Dinggang Shen
Isointense Infant Brain Segmentation by Stacked Kernel Canonical
Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Li Wang, Feng Shi, Yaozong Gao, Gang Li, Weili Lin,
and Dinggang Shen
Improving Accuracy of Automatic Hippocampus Segmentation
in Routine MRI by Features Learned from Ultra-High Field MRI. . . . . . . . . 37
Shuyu Li, Feng Shi, Guangkai Ma, Minjeong Kim, and Dinggang Shen
Dual-Layer ‘ -Graph Embedding for Semi-supervised Image Labeling. . . . . . 46
1
Qian Wang, Guorong Wu, and Dinggang Shen
Automatic Liver Tumor Segmentation in Follow-Up CT Scans:
Preliminary Method and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Refael Vivanti, Ariel Ephrat, Leo Joskowicz, Naama Lev-Cohain,
Onur A. Karaaslan, and Jacob Sosna
Block-Based Statistics for Robust Non-parametric Morphometry. . . . . . . . . . 62
Geng Chen, Pei Zhang, Ke Li, Chong-Yaw Wee, Yafeng Wu,
Dinggang Shen, and Pew-Thian Yap
Automatic Collimation Detection in Digital Radiographs with the Directed
Hough Transform and Learning-Based Edge Detection . . . . . . . . . . . . . . . . 71
Liang Zhao, Zhigang Peng, Klaus Finkler, Anna Jerebko,
Jason J. Corso, and Xiang (Sean) Zhou
Efficient Lung Cancer Cell Detection with Deep Convolution
Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Zheng Xu and Junzhou Huang
VIII Contents
An Effective Approach for Robust Lung Cancer Cell Detection . . . . . . . . . . 87
Hao Pan, Zheng Xu, and Junzhou Huang
Laplacian Shape Editing with Local Patch Based Force Field
for Interactive Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Chaowei Tan, Zhennan Yan, Kang Li, Dimitris Metaxas,
and Shaoting Zhang
Hippocampus Segmentation Through Distance Field Fusion. . . . . . . . . . . . . 104
ShumaoPang,ZhentaiLu,WeiYang,YaoWu,ZixiaoLu,LimingZhong,
and Qianjin Feng
Learning a Spatiotemporal Dictionary for Magnetic Resonance
Fingerprinting with Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Pedro A. Gómez, Cagdas Ulas, Jonathan I. Sperl, Tim Sprenger,
Miguel Molina-Romero, Marion I. Menzel, and Bjoern H. Menze
Fast Regions-of-Interest Detection in Whole Slide Histopathology Images . . . 120
Ruoyu Li and Junzhou Huang
Reliability Guided Forward and Backward Patch-Based Method
for Multi-atlas Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Liang Sun, Chen Zu, and Daoqiang Zhang
Correlating Tumour Histology and ex vivo MRI Using Dense
Modality-Independent Patch-Based Descriptors. . . . . . . . . . . . . . . . . . . . . . 137
Andre Hallack, Bartłomiej W. Papież, James Wilson, Lai Mun Wang,
Tim Maughan, Mark J. Gooding, and Julia A. Schnabel
Multi-atlas Segmentation Using Patch-Based Joint Label Fusion
with Non-Negative Least Squares Regression. . . . . . . . . . . . . . . . . . . . . . . 146
Mattias P. Heinrich, Matthias Wilms, and Heinz Handels
A Spatially Constrained Deep Learning Framework for Detection
of Epithelial Tumor Nuclei in Cancer Histology Images. . . . . . . . . . . . . . . . 154
Korsuk Sirinukunwattana, Shan E. Ahmed Raza, Yee-Wah Tsang,
David Snead, Ian Cree, and Nasir Rajpoot
3D MRI Denoising Using Rough Set Theory and Kernel Embedding
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Ashish Phophalia and Suman K. Mitra
A Novel Cell Orientation Congruence Descriptor for Superpixel
Based Epithelium Segmentation in Endometrial Histology Images. . . . . . . . . 172
Guannan Li, Shan E. Ahmed Raza, and Nasir Rajpoot
Contents IX
Patch-Based Segmentation from MP2RAGE Images: Comparison
to Conventional Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Erhard T. Næss-Schmidt, Anna Tietze, Irene K. Mikkelsen,
Mikkel Petersen, Jakob U. Blicher, Pierrick Coupé, José V. Manjón,
and Simon F. Eskildsen
Multi-atlas and Multi-modal Hippocampus Segmentation for Infant
MR Brain Images by Propagating Anatomical Labels on Hypergraph . . . . . . 188
Pei Dong, Yanrong Guo, Dinggang Shen, and Guorong Wu
Prediction of Infant MRI Appearance and Anatomical Structure Evolution
Using Sparse Patch-Based Metamorphosis Learning Framework . . . . . . . . . . 197
Islem Rekik, Gang Li, Guorong Wu, Weili Lin, and Dinggang Shen
Efficient Multi-scale Patch-Based Segmentation . . . . . . . . . . . . . . . . . . . . . 205
Abinash Pant, David Rivest-Hénault, and Pierrick Bourgeat
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215