Final Workshop Program

8:45 - 9:00 Opening Remarks

9:00 - 10:30 Morning Sessions 1: Plenary Talk

Boosted Segmentation of Neuroanatomical Datasets using Shape Complex Atlases

Prof. Anand Rangarajan, University of Florida

Prof. Anand Rangarajan received the BTech degree in electronics engineering from the Indian Institute of Technology, Madras, India in 1984, and the PhD degree in electrical engineering from the University of Southern California in 1991. From 1990 to 1992, he was a postdoctoral associate in the Departments of Diagnostic Radiology and Computer Science, Yale University. From 1992 to 1995, he held a joint research faculty position in both departments. From 1995 to 2000, he was an assistant professor in the Image Processing and Analysis Group (IPAG), Departments of Diagnostic Radiology and Electrical Engineering, Yale University. He is now an associate professor in the Department of Computer and Information Science and Engineering (CISE), University of Florida. In 1992, he chaired a Neural Information Processing Systems (NIPS) Workshop entitled "Deterministic Annealing and Combinatorial Optimization" and in 1995, he cochaired a NIPS Workshop (with Eric Mjolsness) entitled "Statistical and Structural Models in Network Vision." He has served on the Program Committees of Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) 1997, 1999, and 2001, the Workshop on Biomedical Image Analysis (WBIA) 1998, Statistical and Computational Theories of Vision (SCTV) 1999, Workshop on Biomedical Image Registration (WBIR) 1999, IEEE Workshop on Motion and Video Computing 2002 and IEEE Computer Vision and Pattern Recognition (CVPR) 2000, 2001, and 2003. He has also cochaired the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2001 (with Larry Staib) and EMMCVPR 2003 (with Mario Figueiredo and Josiane Zerubia). His current research interests are best summarized as the application of machine learning ideas and principles to medical image analysis and computer vision. He is also interested in the scientific study of consciousness and has been active in the Tucson series of conferences entitled Toward a Science of Consciousness (1996, 1998, 2000, and 2002). He is member of the IEEE.

10:30-11:00 Coffee break

11:00-12:30 Morning Sessions 2: Image Segmentation

Session Chair: Prof. Mads Nielsen

  • Transductive Prostate Segmentation for CT Image Guided Radiotherapy,
    Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen
  • Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries,
    Yefeng Zheng, Jianhua Shen, Huseyin Tek, Gareth Funka-Lea
  • A Novel 3D Joint MGRF Framework for Precise Lung Segmentation,
    Behnoush Abdollahi, Ahmed Soliman, A. C. Civelek, X.-F. Li, G. Gimel'farb, Ayman El-Baz
  • Simultaneous Registration and Segmentation by L1 Minimization,
    Pratik Shah, Mithun Das Gupta
  • Supervised Image Segmentation across Scanner Protocols: A Transfer Learning Approach
    Annegreet Van Opbroek, Arfan Ikram, Meike Vernooij, Marleen de Bruijne

12:30 - 14:00 Lunch & Posters

  • Group Sparsity Constrained Automatic Brain Label Propagation
    Shu Liao*, Daoqiang Zhang, Pew Thian Yap, Guorong Wu, Dinggang Shen, UNC
  • Sparse Patch-guided Deformation Estimation for Improved Image Registration
    Minjeong Kim*, UNC Chapel Hill Guorong Wu, Dinggang Shen, UNC
  • Computer-aided Detection of Aneurysms in 3D Time-of-Flight MRA Datasets,
    Santiago Suniaga, Rene Werner, Andre Kemmling, Michael Groth, Jens Fiehler, Nils Daniel Forkert*
  • Data Driven Constraints for the SVM,
    Sune Darkner*, Line Clemmesen, DTU
  • Towards improving the accuracy of sensorless freehand 3D ultrasound by learning,
    Juliette Conrath, Catherine Laporte*
  • Use of Pattern-Information Analysis in Vision Science: a Pragmatic Examination,
    Ruiz Mathieu*, jean-Michel Hupe, Michel Dojat
  • Human Age Estimation with Surface-based Features from MRI Images,
    Jieqiong Wang, Dai Dai, Meng Li, Jing Hua, Huiguang He*, CASIA
  • Biomedical Images Classification by Universal Nearest Neighbours Classifier using Posterior Probability,
    Roberto D'Ambrosio*, Paolo Soda, Michel Barlaud, Wafa Bel Ha J Ali, Richard Nock, Frank Nielsen
  • On the creation of generic fMRI feature networks using 3-D moment invariants,
    Loizos Markides*, Imperial College London Duncan Gillies, Imperial College London
  • A Localized MKL method for brain classification with known intra-class variability
    Aydin Ulas*, UNIVR, Mehmet Gonen, Umberto Castellani, Vittorio Murino, Marcella Bellani, Michele Tansella, Paolo Brambilla
  • Learning to Locate Cortical Bone in MRI
    Gerardo Hermosillo*, Siemens Vikas Raykar, Siemens
  • Combining Multiple Image Segmentations by Maximizing Expert Agreement
    Joni-Kristian Kamarainen, Lappeenranta Univ of Tech Lasse Lensu*, Lappeenranta Univ. of Tech. Tomi Kauppi, Lappeenranta Univ of Tech
  • Cardiac LV and RV Segmentation Using Mutual Context Information
    Dwarikanath Mahapatra*, ETH Zurich
  • Non-parametric Density Modeling and Outlier Detection in Medical Imaging Datasets
    Virgile Fritsch*, Inria Gael Varoquaux, Jean-Baptiste Poline, Bertrand Thirion, INRIA / Parietal
  • Gradient Projection Learning for Parametric Nonrigid Registration
    Stefan Pszczolkowski*, Imperial College London Declan O'Regan, Robert Steiner, MRI Unit, Imperial College London. Daniel Rueckert,
  • Integrating Statistical Shape Models into a Graph Cut Framework for Tooth Segmentation
    Johannes Keustermans*, KU Leuven Dirk Vandermeulen, KU Leuven Paul Suetens, KU Leuven
  • A Random Forest based Approach for One Class Classification in Medical Imaging,
    Chesner De sir, Simon Bernard, Caroline Petitjean*, Laurent Heutte,
  • Computer Aided Skin Lesion Diagnosis with Humans in the Loop
    Orod Razeghi*, University of Nottingham Guoping Qiu, University of Nottingham Hywel Williams, University of Nottingham Kim Thomas, University of Nottingham

14:00 - 15:30 Afternoon Sessions 1: Computer-aided Detection/Diagnosis

Session Chair: Prof. Marleen de Bruijne

  • MRI confirmed prostate tissue classification with Laplacian eigenmaps of ultrasound RF spectra,
    Mehdi Moradi, Christian Wachinger, Andriy Fedorov, William Wells, tina Kapur, Luciant Wolfsberg, Paul Nguyen, Clare Tempany
  • Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer's Disease,
    Manhua Liu, Daoqiang Zhang, Pew-Thian Yap, Dinggang Shen
  • Nonlinear Discriminant Graph Embeddings for Detecting White Matter Lesions in FLAIR MRI
    Samuel Kadoury, Guray Erus, Christos Davatzikos
  • Description and Classification of Confocal Endomicroscopic Images for the Automatic Diagnosis of Inflammatory Bowel Disease,
    Sara Couceiro, João Barreto, Pedro Figueiredo, Paulo Freire
  • Learning to Rank from Medical Imaging Data
    Fabian Pedregosa, Alexandre Gramfot, Gael Varoquaux, Bertrand Thirion, Elodie Cauvet, Christophe Paller

15:30- 16:00 Coffee break

16:00 - 17:30 Afternoon Sessions 2: Classification, and Registration

Session Chair: Prof. Daniel Rueckert

  • Dense Deformation Reconstruction via Sparse Coding
    Yonghong Shi, Guorong Wu, Zhijian Song, Dinggang Shen
  • Quality Classification of Microscopic Imagery with Weakly Supervised Learning,
    Xinghua Lou, Luca Fiaschi, Ullrich Koethe, Fred Hamprecht
  • Graph-based inter-subject classification of local fMRI patterns,
    Sylvain Takerkart, Guillaume Auzias, Daniele Schon, Bertrand Thirion, Liva Ralaivola
  • Learning Correspondences in Knee MR images from the Osteoarthritis Initiative
    Ricardo Guerrero, Claire Donoghue, Luis Pizarro, Daniel Rueckert
  • Finding Deformable Shapes by Correspondence-free Instantiation and Registration of Statistical Shape Models,
    Weiguo Xie, Steffen Schumann, Jochen Franke, Paul Alfred Grützner, Lutz-Peter Nolte, Guoyan Zheng

17:30 - 17:45 Closing remarks