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Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes

  • Christine Kroll
  • Fausto Milletari
  • Nassir Navab
  • Seyed-Ahmad Ahmadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

This paper analyses the applicability and performance of Convolutional Neural Networks (CNN) to localise and segment anatomical structures in medical volumes under clinically realistic constraints: small amount of available training data, the need of a short processing time and limited computational resources. Our segmentation approach employs CNNs for simultaneous classification and feature extraction. A Hough voting strategy has been developed in order to automatically localise and segment the anatomy of interest. Our results show (i) improved robustness, due to the inclusion of prior shape knowledge, (ii) highly accurate segmentation even when only small datasets are available during training, (iii) speed and computational requirements that match those that are usually present in clinical settings.

Keywords

Training Volume Convolutional Neural Network Transcranial Ultrasound Segmentation Volume Semantic Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This study was funded by the Lüneburg Heritage and Deutsche Forschungsgesellschaft (DFG) Grant BO 1895/4-1. We gratefully acknowledge the support of NVIDIA Corporation in donating a Tesla K40 GPU for this study.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Christine Kroll
    • 1
  • Fausto Milletari
    • 1
  • Nassir Navab
    • 1
    • 2
  • Seyed-Ahmad Ahmadi
    • 3
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  2. 2.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA
  3. 3.Department of NeurologyKlinikum Grosshadern, Ludwig-Maximilians-Universität MünchenMunichGermany

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