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Fetal skull segmentation in 3D ultrasound via structured geodesic random forest

Cerrolaza JJ, Oktay O, Gomez A, Matthew J, Knight C, Kainz B, Rueckert D (2017)

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Publication Type: Conference contribution

Publication year: 2017

Journal

Publisher: Springer Verlag

Book Volume: 10554 LNCS

Pages Range: 25-32

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319675602

DOI: 10.1007/978-3-319-67561-9_3

Abstract

Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. 3D ultrasound has the potential to reduce the operator dependence. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. Unlike alternative auto-context approaches, this new set of features is efficiently integrated into the same forest using contextual trees. Finally, we use a new structured labels space as alternative to the traditional atomic class labels, able to capture morphological variability of the target organ. Here, we show the potential of this new general framework segmenting the skull in 3D fetal ultrasound volumes, significantly outperforming alternative random forest-based approaches.

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How to cite

APA:

Cerrolaza, J.J., Oktay, O., Gomez, A., Matthew, J., Knight, C., Kainz, B., & Rueckert, D. (2017). Fetal skull segmentation in 3D ultrasound via structured geodesic random forest. In M. Jorge Cardoso, Tal Arbel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 25-32). Quebec City, QC, CAN: Springer Verlag.

MLA:

Cerrolaza, Juan J., et al. "Fetal skull segmentation in 3D ultrasound via structured geodesic random forest." Proceedings of the International Workshop on Fetal and Infant Image Analysis, FIFI 2017 and 4th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. M. Jorge Cardoso, Tal Arbel, Springer Verlag, 2017. 25-32.

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