Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy

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Izvoz citacije: ABNT
POPESCU, Diana ;AMZA, Cătălin Gheorghe;LĂPTOIU, Dan ;AMZA, Gheorghe .
Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 58, n.9, p. 509-516, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/>. Date accessed: 22 sep. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2011.184.
Popescu, D., Amza, C., Lăptoiu, D., & Amza, G.
(2012).
Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy.
Strojniški vestnik - Journal of Mechanical Engineering, 58(9), 509-516.
doi:http://dx.doi.org/10.5545/sv-jme.2011.184
@article{sv-jmesv-jme.2011.184,
	author = {Diana  Popescu and Cătălin Gheorghe Amza and Dan  Lăptoiu and Gheorghe  Amza},
	title = {Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {9},
	year = {2012},
	keywords = {medical imaging; pedicle screw; Hopfield neural network; lumbar vertebra},
	abstract = {In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database.},
	issn = {0039-2480},	pages = {509-516},	doi = {10.5545/sv-jme.2011.184},
	url = {https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/}
}
Popescu, D.,Amza, C.,Lăptoiu, D.,Amza, G.
2012 June 58. Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 58:9
%A Popescu, Diana 
%A Amza, Cătălin Gheorghe
%A Lăptoiu, Dan 
%A Amza, Gheorghe 
%D 2012
%T Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy
%B 2012
%9 medical imaging; pedicle screw; Hopfield neural network; lumbar vertebra
%! Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy
%K medical imaging; pedicle screw; Hopfield neural network; lumbar vertebra
%X In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database.
%U https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/
%0 Journal Article
%R 10.5545/sv-jme.2011.184
%& 509
%P 8
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 58
%N 9
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Popescu, Diana, Cătălin Gheorghe Amza, Dan  Lăptoiu, & Gheorghe  Amza.
"Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy." Strojniški vestnik - Journal of Mechanical Engineering [Online], 58.9 (2012): 509-516. Web.  22 Sep. 2021
TY  - JOUR
AU  - Popescu, Diana 
AU  - Amza, Cătălin Gheorghe
AU  - Lăptoiu, Dan 
AU  - Amza, Gheorghe 
PY  - 2012
TI  - Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.184
KW  - medical imaging; pedicle screw; Hopfield neural network; lumbar vertebra
N2  - In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database.
UR  - https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/
@article{{sv-jme}{sv-jme.2011.184},
	author = {Popescu, D., Amza, C., Lăptoiu, D., Amza, G.},
	title = {Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {9},
	year = {2012},
	doi = {10.5545/sv-jme.2011.184},
	url = {https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/}
}
TY  - JOUR
AU  - Popescu, Diana 
AU  - Amza, Cătălin Gheorghe
AU  - Lăptoiu, Dan 
AU  - Amza, Gheorghe 
PY  - 2018/06/28
TI  - Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 58, No 9 (2012): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.184
KW  - medical imaging, pedicle screw, Hopfield neural network, lumbar vertebra
N2  - In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database.
UR  - https://www.sv-jme.eu/sl/article/competitive-hopfield-neural-network-model-for-evaluating-pedicle-screw-placement-accuracy/
Popescu, Diana, Amza, Cătălin, Lăptoiu, Dan, AND Amza, Gheorghe.
"Competitive Hopfield Neural Network Model for Evaluating Pedicle Screw Placement Accuracy" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 58 Number 9 (28 June 2018)

Avtorji

Inštitucije

  • University Politehnica of Bucharest, Romania 1
  • Colentina Clinical Hospital, Romania 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 58(2012)9, 509-516

https://doi.org/10.5545/sv-jme.2011.184

In this paper, the application of an X-ray image segmentation algorithm based on a Competitive Hopfield Neural Network (CHNN) model for evaluating the insertion accuracy of pedicle screws is presented. In practice, the evaluation of pedicle screw insertion accuracy is made visually in two planes and is based on postoperative computer tomography scans or radiography. In order to increase the reliability of the assessment, this research proposes a new approach that automates this process and can be used for developing a training system for pedicle screw implantation. The proposed approach implements a training method which allows extracting features of the pedicle screw from X-ray images segmented using a modified HNN algorithm, and compares them with values from a knowledge database.

medical imaging; pedicle screw; Hopfield neural network; lumbar vertebra