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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

07.09.2018 | Original Article

FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images

verfasst von: M. Villa, G. Dardenne, M. Nasan, H. Letissier, C. Hamitouche, E. Stindel

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2018

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Abstract

Purpose

A new algorithm, based on fully convolutional networks (FCN), is proposed for the automatic localization of the bone interface in ultrasound (US) images. The aim of this paper is to compare and validate this method with (1) a manual segmentation and (2) a state-of-the-art method called confidence in phase symmetry (CPS).

Methods

The dataset used for this study was composed of 1738 US images collected from three volunteers and manually delineated by three experts. The inter- and intra-observer variabilities of this manual delineation were assessed. Images having annotations with an inter-observer variability higher than a confidence threshold were rejected, resulting in 1287 images. Both FCN-based and CPS approaches were studied and compared to the average inter-observer segmentation according to six criteria: recall, precision, F1 score, accuracy, specificity and root-mean-square error (RMSE).

Results

The intra- and inter-observer variabilities were inferior to 1 mm for 90% of manual annotations. The RMSE was 1.32 ± 3.70 mm and 5.00 ± 7.70 mm for, respectively, the FCN-based approach and the CPS algorithm. The mean recall, precision, F1 score, accuracy and specificity were, respectively, 62%, 64%, 57%, 80% and 83% for the FCN-based approach and 66%, 34%, 41%, 52% and 43% for the CPS algorithm.

Conclusion

The FCN-based approach outperforms the CPS algorithm, and the obtained RMSE is similar to the manual segmentation uncertainty.

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Titel: FCN-based approach for the automatic segmentation of bone surfaces in ultrasound images
verfasst von: M. Villa
G. Dardenne
M. Nasan
H. Letissier
C. Hamitouche
E. Stindel
Publikationsdatum: 07.09.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1856-x

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Abstract

Purpose

Methods

Results

Conclusion

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