Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/123156
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Type: Journal article
Title: Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images
Author: Dunnhofer, M.
Antico, M.
Sasazawa, F.
Takeda, Y.
Camps, S.
Martinel, N.
Micheloni, C.
Carneiro, G.
Fontanarosa, D.
Citation: Medical Image Analysis, 2020; 60:101631-1-101631-17
Publisher: Elsevier
Issue Date: 2020
ISSN: 1361-8415
1361-8423
Statement of
Responsibility: 
Matteo Dunnhofer, Maria Antico, Fumio Sasazawa, Yu Takeda, Saskia Camps, Niki Martinel, Christian Micheloni, Gustavo Carneiro, Davide Fontanarosa
Abstract: The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images. Through extensive performance validation given by the Dice Similarity Coefficient, we demonstrate that our algorithm is able to track the femoral condyle cartilage with an accuracy which is comparable to experienced surgeons. It is additionally shown that the proposed method outperforms state-of-the-art segmentation models and trackers in the localization of the cartilage. We claim that the proposed solution has the potential for ultrasound guidance in minimally invasive knee procedures.
Keywords: Knee arthroscopy; knee cartilage; ultrasound; ultrasound guidance; visual tracking; fully convolutional siamese networks; deep learning
Rights: Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.
RMID: 1000012534
DOI: 10.1016/j.media.2019.101631
Grant ID: http://purl.org/au-research/grants/arc/DP180103232
Appears in Collections:Computer Science publications

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