Machine Learning in Medical Imaging

1. Front Matter

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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 et al.

   Pages 1-9

3. 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, Mauricio Reyes

   Pages 10-17

4. Content-Based Medical Image Retrieval with Metric Learning via Rank Correlation

   • Wei Huang, Kap Luk Chan, Huiqi Li, Joo Hwee Lim, Jiang Liu, Tien Yin Wong

   Pages 18-25

5. A Hyper-parameter Inference for Radon Transformed Image Reconstruction Using Bayesian Inference

   • Hayaru Shouno, Masato Okada

   Pages 26-33

6. Patch-Based Generative Shape Model and MDL Model Selection for Statistical Analysis of Archipelagos

   • Melanie Ganz, Mads Nielsen, Sami Brandt

   Pages 34-41

7. 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 et al.

   Pages 42-49

8. Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis

   • Vincent Michel, Evelyn Eger, Christine Keribin, Bertrand Thirion

   Pages 50-57

9. Appearance Normalization of Histology Slides

   • Marc Niethammer, David Borland, J. S. Marron, John Woosley, Nancy E. Thomas

   Pages 58-66

10. Parallel Mean Shift for Interactive Volume Segmentation

    • Fangfang Zhou, Ying Zhao, Kwan-Liu Ma

    Pages 67-75

11. 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 et al.

    Pages 76-83

12. Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization

    • Yefeng Zheng, Fernando Vega-Higuera, Shaohua Kevin Zhou, Dorin Comaniciu

    Pages 84-91

13. Relation-Aware Spreadsheets for Multimodal Volume Segmentation and Visualization

    • Lin Zheng, Yingcai Wu, Kwan-liu Ma

    Pages 92-99

14. A Bayesian Learning Application to Automated Tumour Segmentation for Tissue Microarray Analysis

    • Ching-Wei Wang

    Pages 100-107

15. Generalized Sparse Classifiers for Decoding Cognitive States in fMRI

    • Bernard Ng, Arash Vahdat, Ghassan Hamarneh, Rafeef Abugharbieh

    Pages 108-115

16. Manifold Learning for Biomarker Discovery in MR Imaging

    • Robin Wolz, Paul Aljabar, Joseph V. Hajnal, Daniel Rueckert

    Pages 116-123

17. Optimal Live Cell Tracking for Cell Cycle Study Using Time-Lapse Fluorescent Microscopy Images

    • Fuhai Li, Xiaobo Zhou, Stephen T. C. Wong

    Pages 124-131

18. Fully Automatic Joint Segmentation for Computer-Aided Diagnosis and Planning

    • André Gooßen, Thomas Pralow, Rolf-Rainer Grigat

    Pages 132-139

19. Accurate Identification of MCI Patients via Enriched White-Matter Connectivity Network

    • Chong-Yaw Wee, Pew-Thian Yap, Jeffery N. Brownyke, Guy G. Potter, David C. Steffens, Kathleen Welsh-Bohmer et al.

    Pages 140-147

20. Feature Extraction for fMRI-Based Human Brain Activity Recognition

    • Wei Bian, Jun Li, Dacheng Tao

    Pages 148-156

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, 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; the- fore tasks in medical imaging require learning from examples to simulate a physician’s prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works 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 range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.

• MRI
• anatomy
• computer-aided diagnosis
• image analysis
• image database retrieval
• image reconstruction
• image segmentation
• image-guided therapy
• machine learning
• medical imaging
• neural networks
• pathologic image analysis
• pattern re
• ultrasound imaging
• algorithm analysis and problem complexity

• IBM Research Almaden, San Jose, USA

  Fei Wang

• Philips Research North America, Briarcliff Manor, USA

  Pingkun Yan

• The University of Chicago, Chicago, USA

  Kenji Suzuki

• University of North Carolina, Chapel Hill, USA

  Dinggang Shen

Policies and ethics