International Workshop on

Machine Learning in Medical Imaging (MLMI)

In conjunction with MICCAI 2010

China National Convention Center in Beijing, China

September 20, 2010

Introduction

Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, image-guided therapy, image annotation and image database retrieval. Machine Learning in Medical Imaging (MLMI) 2010 is the first workshop of MICCAI on this topic. This workshop focuses on major trends and challenges in this area, and work to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The technical program will consist of previously unpublished, contributed, and invited papers, with a substantial time allocated to discussion. In addition, we encourage submissions that address innovative research and development in the analysis of medical image data using machine learning techniques.

News:
Key Dates:
  • Paper Submission: June 1, 2010  Extended to the midnight of June 14, 2010 PST (3am, June 15 EST; 3pm, June 15 Beijing Time)
  • Notification of Acceptance: July 15, 2010
  • Camera-ready Version: July 22, 2010
  • Workshop: September 20, 2010
Organizers:

Call For Papers

Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, image-guided therapy, image annotation and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance tomography and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient’s imaging data is often not sufficient to provide satisfactory performance, therefore tasks in medical imaging require learning from examples to simulate physician’s prior knowledge of the data.

Researchers are now beginning to use techniques such as modern implementations of supervised, unsupervised, semi-supervised and reinforcement learning, for instance using probabilistic modeling and kernel methods. The main aim of this workshop is to help advance the scientific research within the broad field of medical imaging and machine learning. This workshop focuses on major trends and challenges in this area, and work to identify new techniques and their use in medical imaging. We are looking for original, high-quality submissions that address innovative research and development in the analysis of medical image data using machine learning techniques.

Topics of interests include but are not limited to:

  • Machine learning (e.g., with support vector machines, statistical methods, manifold-space-based methods, artificial neural networks) applications to medical images with 2D, 3D and 4D data
  • Medical image analysis (e.g., pattern recognition, classification, segmentation, registration) of anatomical structures and lesions
  • Multi-modality fusion (e.g., MRI, PET, CT projection X-ray, CT, X-ray, ultrasound) for image guided interventions
  • Image reconstruction for medical imaging (e.g., CT, PET, MRI, X-ray)
  • Computer-aided detection/diagnosis (e.g., for lung cancer, prostate cancer, breast cancer, colon cancer, liver cancer, acute disease, chronic disease, osteoporosis)
  • Medical image retrieval (e.g., context-based retrieval)
  • Cellular image analysis (e.g., genotype, phenotype, classification, identification, cell tracking)
  • Molecular/pathologic image analysis
  • Dynamic, functional, physiologic, and anatomic imaging

Download the workshop flyer in PDF format

Submission Guidelines

Proceedings: The MLMI 2010 proceedings will be published as a volume (vol. 6357) in the Springer Lecture Notes in Computer Science (LNCS) series.

Paper Formatting: Papers are limited to eight pages. Papers should be formatted in Lecture Notes in Computer Science style. Style files can be found on the Springer website. The file format for submissions is Adobe Portable Document Format (PDF). Other formats will not be accepted.

Blind review: MLMI reviewing is double blind: authors do not know the names of the reviewers of their papers, and reviewers do not know the names of the authors. Please see the Anonymity guidelines of MICCAI 2010 for detailed explanations of how to ensure this.

Submission: MLMI is using an online submission system. - Paper submission is closed -

Supplemental material: Supplemental material submission is optional. This material may include: videos of results that cannot be included in the main paper, anonymized related submissions to other conferences and journals, and appendices or technical reports containing extended proofs and mathematical derivations that are not essential for understanding of the paper. Contents of the supplemental material should be referred to appropriately in the paper and that reviewers are not obliged to look at it.

Simultaneous submissions: Our policy is that in submitting a paper, authors implicitly acknowledge that NO paper of substantially similar content has been or will be submitted to another conference or workshop until MLMI decisions are made.

Camera-ready Submission MLMI is using the same site for the final submission. Please follow the instructions from the Springer website to prepare your final submission. Authors of each accepted paper needs to upload a single zip file including the following materials: 1. a completed copyright form (not required if your paper has not been selected as part of the LNCS proceedings), 2. a PDF of the camera ready, and 3. source files of the camera ready, namely, a Word file or a .tex file plus all figures, style files, special fonts, .eps, .bib etc.

Registration

Please register for the MLMI workshop through the MICCAI registration system.  Make sure to select the MLMI workshop (M-W1).

Technical Program

Plenary Speakers:

Final Agenda:

  • 8:50am - 9:00am Welcome
  • 9:00am - 10:00am Oral Session 1: Medical Image Segmentation
        Session Chair: Mads Nielsen
  1. Parallel Mean Shift for Interactive Volume Segmentation
    Fangfang Zhou, Ying Zhao, and Kwan-Liu Ma
  2. Soft Tissue Discrimination using Magnetic Resonance Elastography with a New Elastic Level Set Model
    Bing Nan Li, Chee Kong Chui, Sim Heng Ong, Toshikatsu Washio, Tomokazu Numano, Stephen Chang, Sudhakar Venkatesh, and Etsuko Kobayashi
  3. Relation-Aware Spreadsheet for Multimodal Volume Segmentation and Visualization
    Lin Zheng, Yingcai Wu, and Kwan-liu Ma
  • 10:00am - 10:20am Coffee break
  • 10:20am - 11:20am Invited talk given by Dr. Milan Sonka
  • 11:20am - 12:00pm Oral Session 2: Shape Modeling
        Session Chair: Marc Niethammer
  1. Patch-based Generative Shape Model and MDL Model Selection for Statistical Analysis of Archipelagos
    Melanie Ganz, Mads Nielsen, and Sami Brandt
  2. Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization
    Yefeng Zheng, Fernando Vega-Higuera, S. Kevin Zhou, and Dorin Comaniciu
  • 12:00pm - 2:00pm Lunch + Poster Session
  • 2:00pm - 3:00pm Oral Session 3: fMRI Analysis
        Session Chair: Natasha Lepore
  1. Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
    Vincent Michel, Evelyn Eger, Christine Keribin, and Bertrand Thirion
  2. Generalized Sparse Classifiers for Decoding Cognitive States in fMRI
    Bernard Ng, Arash Vahdat, Ghassan Hamarneh, and Rafeef Abugharbieh
  3. Feature Extraction for fMRI-based Human Brain Activity Recognition
    Wei Bian, Jun Li, and Dacheng Tao
  • 3:00pm - 3:30pm Coffee break
  • 3:30pm - 4:50pm Oral Session 4: Clinical Decision Support
        Session Chair: Alison Noble
  1. Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries
    Xiao Dong, Huanxiang Lu, Yasuo Sakurai, Hitoshi Yamagata, Guoyan Zheng, and Mauricio Reyes
  2. Content-based Medical Image Retrieval with Metric Learning via Rank Correlation
    Wei Huang, Kap Luk Chan, Huiqi Li, Joo Hwee Lim, Jiang Liu, and Tien Yin Wong
  3. Manifold Learning for Biomarker Discovery in MR imaging
    Robin Wolz, Paul Aljabar, Joseph V. Hajnal, and Daniel Rueckert
  4. Principal-Component Massive-Training Machine-Learning Regression for False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography
    Kenji Suzuki, Jianwu Xu, Jun Zhang, and Ivan Sheu
  • 4:50pm - 5:00pm Award announcement and closing remarks

For oral presenters:  Each paper has 15 minutes for presentation and 5 minutes for Q & A.  Presenters should report to their session chair before each session no later than the following times: Session 1 - 8:45am; Session 2 - 10:15am; Session 3 - 1:55pm; Session 4 - 3:25pm.

Posters:

  1. Automated Localization of Solid Organs in 3D CT Images: A Majority Voting Algorithm Based on Ensemble Learning
    Xiangrong Zhou,Syunichi Yoshimoto,Song Wang,Huayue Chen, Takeshi Hara, Ryujiro Yokoyama, Masayuki Kanematsu, and Hiroshi Fujita
  2. Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images
    Sushil Mittal, Yefeng Zheng, Bogdan Georgescu, Fernando Vega-Higuera, Shaohua Kevin Zhou, Peter Meer, and Dorin Comaniciu
  3. Bayesian Classification of Local 3D Structures in Medical Images
    Yiyi Miao, Mehran Kafai, and Kazunori Okada
  4. A Hyper-parameter Inference for Radon Transformed Image Reconstruction Using Bayesian Inference
    Hayaru Shouno, and Masato Okada
  5. Prediction of Dementia by Hippocampal Shape Analysis
    Hakim C. Achterberg, Fedde van der Lijn, Tom den Heijer, Aad van der Lugt, Monique M.B. Breteler, Wiro J. Niessen, and Marleen de Bruijne
  6. Appearance Normalization of Histology Slides
    Marc Niethammer, David Borland, J. S. Marron, John Woosley, and Nancy E. Thomas
  7. Using K-Means clustering and MI for Non-rigid Registration of MRI and CT
    Yixun Liu, and Nikos Chrisochoides
  8. A 3D Statistical Fluid Registration Algorithm
    Caroline Brun, Natasha Lepore, Xavier Pennec, Yi-Yu Chou, Greig de Zubicaray, Katie McMahon, Margaret Wright, James Gee, and Paul Thompson
  9. A Bayesian Learning Application to Automated Tumour Segmentation for Tissue Microarray Analysis
    Ching-Wei Wang
  10. Nucleus Classification and Bile Duct Detection in Liver Histology
    Phatthanaphong Chomphuwiset, Derek Magee, Roger Boyle, and Darren Treanor
  11. Estimation of Posterior Marginal Distribution of Each Point in Registration of Point Distribution Model
    Hidekata Hontani, and Wataru Watanabe
  12. Boosted-LDA for Biomedical Data Analysis
    Arturo Flores, Marius Linguraru, and Kazunori Okada
  13. Optimal Live Cell Tracking for Cell Cycle Study Using Time-lapse Fluorescent Microscopy Images
    Fuhai Li, Xiaobo Zhou, and Stephen T.C. Wong
  14. Fully Automatic Joint Segmentation for Computer-Aided Diagnosis and Planning
    André Gooßen, Thomas Pralow, and Rolf-Rainer Grigat
  15. Accurate Identification of MCI Patients Via Enriched White-Matter Connectivity Network
    Chong-Yaw Wee, Pew-Thian Yap, Jeffery N. Browndyke, Guy G. Potter, David C. Steffens, Kathleen Welsh-Bohmer, Lihong Wang, and Dinggang Shen
  16. Sparse Spatio-Temporal Inference of Electromagnetic Brain Sources
    Carsten Stahlhut, Hagai T. Attias, David Wipf, Lars K. Hansen, and Srikantan S. Nagarajan
  17. Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis
    Yiming Xiao, Mohak Shah, Simon Francis, Douglas L. Arnold, Tal Arbel, and D. Louis Collins
  18. Preliminary Study on Appearance-based Detection of Anatomical Point Landmarks in Body Trunk CT Images
    Mitsutaka Nemoto, Yukihiro Nomura, Shohei Hanaoka, Yoshitaka Masutani, Takeharu Yoshikawa, Naoto Hayashi, Naoki Yoshioka, and Kuni Ohtomo

For poster presenters:  The physical dimensions of the poster board will be 90cm in width and 120cm in height.  Please prepare your poster accordingly.

After Workshop

Proceedings: The MLMI 2010 proceedings has been published as a volume (vol. 6357), Machine Learning in Medical Imaging, in the Springer Lecture Notes in Computer Science (LNCS) series.

The Best Paper Award: The MLMI 2010 presented the Best Paper Award to Wei Huang, Kap Luk Chan, Huiqi Li, Joo Hwee Lim, Jiang Liu, and Tien Yin Wong in recognition of excellence of their paper entitled Content-based Medical Image Retrieval with Metric Learning via Rank Correlation. The rigorous selection process is summarized as follows:  Three papers with the highest three average scores given by three reviewers on the program committee in the double-blinded review process were selected as finalists.  Four organizers reviewed and rated final submitted papers of the finalists before the workshop as well as their presentations including questions and answers during the workshop.  The final decision was made by taking both paper and presentation qualities into account.  Since all papers were excellent, it was not an easy decision.

Statistics: A total of 38 papers were submitted to the workshop in response to the call for papers.  Each of the 38 papers underwent a rigorous double-blinded peer-review process, with each paper being reviewed by at least two (typically three) external reviewers in the program committee composed of over 30 known experts in the field.  Based on the reviewing scores and critics, a total of the 30 best papers were accepted and included in the MICCAI Workshop Proceedings.  Among them, the 23 best papers (60%) were chosen to be included in this Springer LNCS volume; 12 papers (30%) were selected for oral presentations.

Photos: Please enjoy a couple of photos taken during the workshop.

People

Organizers:

Program Committee

  • Vince D. Calhoun, University of New Mexico, USA
  • Heang-Ping Chan, University of Michigan Medical Center, USA
  • Marleen de Bruijne, University of Copenhagen, Denmark
  • James Duncan, Yale University, USA
  • Alejandro Frangi, Pompeu Fabra University
  • Joachim Hornegger, Friedrich-Alexander University, Germany
  • Steve B. Jiang, University of California, San Diego, USA
  • Xiaoyi Jiang, University of Münster, Germany
  • Ghassan Hamarneh, Simon Fraser University, Canada
  • Nico Karssemeijer, Radboud University Nijmegen Medical Centre, The Netherlands
  • Shuo Li, GE Healthcare, Canada
  • Marius Linguraru, National Institutes of Health, USA
  • Yoshitaka Masutani, University of Tokyo, Japan
  • Janne Nappi, Harvard Medical School, USA
  • Mads Nielsen, University of Copenhagen, Denmark
  • Sebastien Ourselin, University College London, UK
  • Daniel Rueckert, Imperial College London, UK
  • Clarisa Sanchez, University Medical Center Utrecht, The Netherlands
  • Kuntal Sengupta, MERL Research, USA
  • Akinobu Shimizu, Tokyo Univ. Agriculture and Technology, Japan
  • Dave Tahmoush, US Army Research Laboratory, USA
  • Hotaka Takizawa, University of Tsukuba, Japan
  • Xiaodong Tao, GE Global Research, USA
  • Georgia D. Tourassi, Duke University, USA
  • Zhuowen Tu, Univ. Califonia, Los Angeles, USA
  • Bram van Ginneken, Radboud University Nijmegen Medical Centre, The Netherlands
  • Guorong Wu, University of North Carolina, Chapel Hill, USA
  • Jianwu Xu, University of Chicago, USA
  • Jane You, Hong Kong Polytechnic University, China
  • Bin Zheng, University of Pittsburgh, USA
  • Guoyan Zheng, University of Bern, Switzerland
  • Kevin Zhou, Siemens Corporate Research, USA
  • Sean Zhou, Siemens Medical Solutions, USA