Multi-structure Atlas-Based Segmentation Using Anatomical Regions of Interest

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• 作者：脫scar Alfonso Jim茅nez聽del聽Toro (21)
  Henning M眉ller (21) (22)
  
  21. University of Applied Sciences Western Switzerland (HES-SO) ; Sierre ; Switzerland
  22. University Hospitals and University of Geneva ; Geneva ; Switzerland
  
• 关键词：Visceral ; Atlas ; based segmentation ; Image registration
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8331
• 期：1
• 页码：217-221
• 全文大小：184 KB
• 参考文献：1. Langs, G, M眉ller, H, Menze, BH, Hanbury, A VISCERAL: towards large data in medical imaging-challenges and directions. In: Greenspan, H, M眉ller, H, Syeda-Mahmood, T eds. (2013) MCBR-CDS 2012. Springer, Heidelberg, pp. 92-98
  2. Rohlfing, T, Brandt, R, Menzel, R, Maurer, CR Jr (1999) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 23: pp. 983-994
  3. Klein, S, Staring, M, Murphy, K, Viergever, MA, Pluim, JPW (2010) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29: pp. 196-205 CrossRef
  4. Klein, S, Pluim, JPW, Staring, M, Viergever, MA (2009) Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis. 81: pp. 227-239 CrossRef
  5. Warfield, SK, Zou, KH, Wells, WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23: pp. 903-921 CrossRef
  
• 作者单位：Medical Computer Vision. Large Data in Medical Imaging
• 丛书名：978-3-319-05529-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The Visceral project organizes a benchmark on multiple anatomical structure segmentation. A training set is provided to the participants that includes a sample of the manual annotations of these structures. To evaluate different segmentation approaches a testing set of volumes must be segmented automatically in a limited period of time. A multi-atlas based segmentation approach is proposed. This technique can be implemented automatically and applied to different anatomical structures with a large enough training set. The addition of a hierarchical local alignment based on anatomical knowledge and local contrast is explained in the approach. An initial experiment to evaluate the impact of using a local alignment and its results show a higher overlap ( \({>}9.7\,\%\) ) of the structures measured with the Jaccard coefficient. The approach is an effective and easy to implement method that adjusts well to the Visceral benchmark.