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Deformation Models pp 209–235Cite as

Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach

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Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 7))

Abstract

Medical image processing tools are playing an increasingly important role in assisting the clinicians in diagnosis, therapy planning and image-guided interventions. Accurate, robust and fast tracking of deformable anatomical objects, such as the heart, is a crucial task in medical image analysis. One of the main challenges is to maintain an anatomically consistent representation of target appearance that is robust enough to cope with inherent changes due to target movement, imaging device movement, varying imaging conditions, and is consistent with the domain expert clinical knowledge. To address these challenges, this chapter presents a probabilistic framework that relies on anatomically indexed component-based object models which integrate several sources of information to determine the temporal trajectory of the deformable target. Large annotated imaging databases are exploited to encode the domain knowledge in shape models and motion models and to learn discriminative image classifiers for the target appearance. The framework robustly fuses the prior information with traditional tracking approaches based on template matching and registration. We demonstrate various medical image analysis applications with focus on cardiology such as 2D auto left heart, catheter detection and tracking, 3D cardiac chambers surface tracking, and 4D complex cardiac structure tracking, in multiple modalities including Ultrasound (US), cardiac Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and X-ray fluoroscopy.

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Acknowledgments

The authors would like to thank Dr. David Sahn and Dr. Muhammad Ashraf at OHSU for providing the volumetric ultrasound sequences and sonomicrometry data in the in vitro animal study, Dr. Alan Katz from St. Francis Hospital and Dr. Mani Vannan from OSU Medical Center for fruitful interactions and guidance, and SCR colleagues for helpful discussions.

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Authors and Affiliations

  1. Imaging and Computer Vision, Siemens Corporate Research, Princeton, NJ, 08540, USA

    Yang Wang, Bogdan Georgescu, Terrence Chen, Wen Wu, Peng Wang, Xiaoguang Lu, Razvan Ionasec, Yefeng Zheng & Dorin Comaniciu

Authors
  1. Yang Wang
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  2. Bogdan Georgescu
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  3. Terrence Chen
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  4. Wen Wu
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  5. Peng Wang
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  6. Xiaoguang Lu
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  7. Razvan Ionasec
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  8. Yefeng Zheng
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  9. Dorin Comaniciu
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Corresponding author

Correspondence to Bogdan Georgescu .

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Editors and Affiliations

  1. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa Km. 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Manuel González Hidalgo

  2. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa km. 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Arnau Mir Torres

  3. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa km 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Javier Varona Gómez

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Wang, Y. et al. (2013). Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach. In: González Hidalgo, M., Mir Torres, A., Varona Gómez, J. (eds) Deformation Models. Lecture Notes in Computational Vision and Biomechanics, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5446-1_9

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  • DOI: https://doi.org/10.1007/978-94-007-5446-1_9

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-5445-4

  • Online ISBN: 978-94-007-5446-1

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