Robust segmentation of various anatomies in 3D ultrasound using hough forests and learned data representations
Milletari F, Ahmadi SA, Kroll C, Hennersperger C, Tombari F, Shah A, Plate A, Boetzel K, Navab N (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9350
Pages Range: 111-118
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319245706
DOI: 10.1007/978-3-319-24571-3_14
Abstract
3D ultrasound segmentation is a challenging task due to image artefacts, low signal-to-noise ratio and lack of contrast at anatomical boundaries. Current solutions usually rely on complex, anatomy-specific regularization methods to improve segmentation accuracy. In this work, we propose a highly adaptive learning-based method for fully automatic segmentation of ultrasound volumes. During training, anatomy-specific features are obtained through a sparse auto-encoder. The extracted features are employed in a Hough Forest based framework to retrieve the position of the target anatomy and its segmentation contour. The resulting method is fully automatic, i.e. it does not require any human interaction, and can robustly and automatically adapt to different anatomies yet enforcing appearance and shape constraints.We demonstrate the performance of the method for three different applications: segmentation of midbrain, left ventricle of the heart and prostate.
Involved external institutions
Germany (DE)How to cite
APA:
Milletari, F., Ahmadi, S.-A., Kroll, C., Hennersperger, C., Tombari, F., Shah, A.,... Navab, N. (2015). Robust segmentation of various anatomies in 3D ultrasound using hough forests and learned data representations. In Joachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111-118). Munich, DEU: Springer Verlag.
MLA:
Milletari, Fausto, et al. "Robust segmentation of various anatomies in 3D ultrasound using hough forests and learned data representations." Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Joachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab, Springer Verlag, 2015. 111-118.
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