Coupling convolutional neural networks and hough voting for robust segmentation of ultrasound volumes
Kroll C, Milletari F, Navab N, Ahmadi SA (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9796 LNCS
Pages Range: 439-450
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Hannover, DEU
ISBN: 9783319458854
DOI: 10.1007/978-3-319-45886-1_36
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.
Involved external institutions
Germany (DE)How to cite
APA:
Kroll, C., Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). Coupling convolutional neural networks and hough voting for robust segmentation of ultrasound volumes. In Bjoern Andres, Bodo Rosenhahn (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 439-450). Hannover, DEU: Springer Verlag.
MLA:
Kroll, Christine, et al. "Coupling convolutional neural networks and hough voting for robust segmentation of ultrasound volumes." Proceedings of the 38th German Conference on Pattern Recognition, GCPR 2016, Hannover, DEU Ed. Bjoern Andres, Bodo Rosenhahn, Springer Verlag, 2016. 439-450.
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