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

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 confi...

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Bibliographic Details
Published in:International journal for computer assisted radiology and surgery Vol. 13; no. 11; pp. 1707 - 1716
Main Authors: Villa, M., Dardenne, G., Nasan, M., Letissier, H., Hamitouche, C., Stindel, E.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.11.2018
Springer Nature B.V
Subjects:
Algorithms
Annotations
Artificial neural networks
Bone and Bones - diagnostic imaging
Computer Imaging
Computer Science
Health Informatics
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging
Medicine
Medicine & Public Health
Observer Variation
Original Article
Pattern Recognition and Graphics
Radiology
Recall
Reproducibility of Results
Root-mean-square errors
Sensitivity and Specificity
Surgery
Ultrasonic imaging
Ultrasonography - methods
Vision
Fully conventional network
Computer-assisted orthopedic surgery
Bone
Segmentation
Ultrasound
ISSN:1861-6410, 1861-6429, 1861-6429
Online Access:Get full text
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Summary: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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ISSN:1861-6410
1861-6429
1861-6429
DOI:10.1007/s11548-018-1856-x