Locating anatomical landmarks for prosthetics design using ensemble neural networks

Daniel Jimenez; Thomas Darm; Bill Rogers; Nicolas Walsh

doi:10.1109/ICNN.1997.611641

Locating anatomical landmarks for prosthetics design using ensemble neural networks

Daniel Jimenez, Thomas Darm, Bill Rogers, Nicolas Walsh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Computer aided design of a prosthesis for a below-the-knee (trans-tibial) amputee begins with a digitized representation of the shape of the residual limb. Certain anatomical landmarks must be located on this shape to identify optimal areas for load and pressure relief. A method of locating the midpoint of the patellar tendon, the distal end of the tibia and the head of the fibula is presented. The method involves training ensembles of neural networks on shapes for which the markers have been located manually; the neural networks are then used to find the landmarks for arbitrary shapes. Experimental results show that the method is at least as accurate as a trained prosthetist.

Original language	English (US)
Title of host publication	1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages	81-85
Number of pages	5
DOIs	https://doi.org/10.1109/ICNN.1997.611641
State	Published - 1997
Event	1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States Duration: Jun 9 1997 → Jun 12 1997

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	1
ISSN (Print)	1098-7576

Conference

Conference	1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/Territory	United States
City	Houston, TX
Period	6/9/97 → 6/12/97

ASJC Scopus subject areas

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10.1109/ICNN.1997.611641

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Locating anatomical landmarks for prosthetics design using ensemble neural networks. / Jimenez, Daniel; Darm, Thomas; Rogers, Bill et al.
1997 IEEE International Conference on Neural Networks, ICNN 1997. 1997. p. 81-85 611641 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jimenez, D, Darm, T, Rogers, B & Walsh, N 1997, Locating anatomical landmarks for prosthetics design using ensemble neural networks. in 1997 IEEE International Conference on Neural Networks, ICNN 1997., 611641, IEEE International Conference on Neural Networks - Conference Proceedings, vol. 1, pp. 81-85, 1997 IEEE International Conference on Neural Networks, ICNN 1997, Houston, TX, United States, 6/9/97. https://doi.org/10.1109/ICNN.1997.611641

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abstract = "Computer aided design of a prosthesis for a below-the-knee (trans-tibial) amputee begins with a digitized representation of the shape of the residual limb. Certain anatomical landmarks must be located on this shape to identify optimal areas for load and pressure relief. A method of locating the midpoint of the patellar tendon, the distal end of the tibia and the head of the fibula is presented. The method involves training ensembles of neural networks on shapes for which the markers have been located manually; the neural networks are then used to find the landmarks for arbitrary shapes. Experimental results show that the method is at least as accurate as a trained prosthetist.",

author = "Daniel Jimenez and Thomas Darm and Bill Rogers and Nicolas Walsh",

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language = "English (US)",

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AU - Darm, Thomas

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AB - Computer aided design of a prosthesis for a below-the-knee (trans-tibial) amputee begins with a digitized representation of the shape of the residual limb. Certain anatomical landmarks must be located on this shape to identify optimal areas for load and pressure relief. A method of locating the midpoint of the patellar tendon, the distal end of the tibia and the head of the fibula is presented. The method involves training ensembles of neural networks on shapes for which the markers have been located manually; the neural networks are then used to find the landmarks for arbitrary shapes. Experimental results show that the method is at least as accurate as a trained prosthetist.

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Locating anatomical landmarks for prosthetics design using ensemble neural networks

Abstract

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