TY - JOUR
T1 - Siam-U-Net
T2 - encoder-decoder siamese network for knee cartilage tracking in ultrasound images
AU - Dunnhofer, Matteo
AU - Antico, Maria
AU - Sasazawa, Fumio
AU - Takeda, Yu
AU - Camps, Saskia
AU - Martinel, Niki
AU - Micheloni, Christian
AU - Carneiro, Gustavo
AU - Fontanarosa, Davide
N1 - Funding Information:
This work was partially supported by the Australia-India strategic research fund AISRF53820 (Intelligent Robotic Imaging System for keyhole surgeries) and by the Australian Research Council project ( DP180103232 ). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research
Publisher Copyright:
© 2019
PY - 2020/2
Y1 - 2020/2
N2 - 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.
AB - 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.
KW - Deep learning
KW - Fully convolutional siamese networks
KW - Knee arthroscopy
KW - Knee cartilage
KW - Ultrasound
KW - Ultrasound guidance
KW - Visual tracking
UR - http://www.scopus.com/inward/record.url?scp=85077514053&partnerID=8YFLogxK
U2 - 10.1016/j.media.2019.101631
DO - 10.1016/j.media.2019.101631
M3 - Article
C2 - 31927473
AN - SCOPUS:85077514053
SN - 1361-8415
VL - 60
SP - 1
EP - 17
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 101631
ER -