Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery

YingLiang Ma; Giulio Dagnino; Ioannis Georgilas; Sanja Dogramadzi

doi:10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery

YingLiang Ma, Giulio Dagnino, Ioannis Georgilas, Sanja Dogramadzi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

Long-bone fractures such as femur fractures are very common in trauma centers. Robotic assisted fracture surgery (RAFS) can facilitate the minimally invasive surgery which reduces scarring, infection risk and long hospital stays. One important step in RAFS is to establish the coordinate system link between patient joint (rigidly connected with the robotic system) and an external tracking system. As X-ray fluoroscopic images are routinely used during the procedure, an automatic method is proposed to detect and localize landmarks on the tracking tool using live X-ray image. The proposed method uses combination of block detection, geometric model matching and principle component analysis. A successful rate of 91.3% is achieved after testing on 650 X-ray images and accuracy is within 0.5 mm.

Original language	English
Title of host publication	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Publisher	IEEE
ISBN (Electronic)	978-1-5386-3066-2
DOIs	https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136
Publication status	Published - 2017
Externally published	Yes
Event	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Exeter, United Kingdom Duration: 21 Jun 2017 → 23 Jun 2017

Conference

Conference	2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Country/Territory	United Kingdom
City	Exeter
Period	21/06/17 → 23/06/17

Keywords

computer vision
robotics
computer assisted surgery

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10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

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Ma, Y., Dagnino, G., Georgilas, I., & Dogramadzi, S. (2017). Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery. In 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) IEEE. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

Ma, YingLiang ; Dagnino, Giulio ; Georgilas, Ioannis ; Dogramadzi, Sanja. / Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2017.

@inproceedings{df826f2858be46a5a241622d67ea578b,

title = "Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery",

abstract = "Long-bone fractures such as femur fractures are very common in trauma centers. Robotic assisted fracture surgery (RAFS) can facilitate the minimally invasive surgery which reduces scarring, infection risk and long hospital stays. One important step in RAFS is to establish the coordinate system link between patient joint (rigidly connected with the robotic system) and an external tracking system. As X-ray fluoroscopic images are routinely used during the procedure, an automatic method is proposed to detect and localize landmarks on the tracking tool using live X-ray image. The proposed method uses combination of block detection, geometric model matching and principle component analysis. A successful rate of 91.3% is achieved after testing on 650 X-ray images and accuracy is within 0.5 mm.",

keywords = "computer vision, robotics, computer assisted surgery",

author = "YingLiang Ma and Giulio Dagnino and Ioannis Georgilas and Sanja Dogramadzi",

year = "2017",

doi = "10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136",

language = "English",

booktitle = "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ; Conference date: 21-06-2017 Through 23-06-2017",

}

Ma, Y, Dagnino, G, Georgilas, I & Dogramadzi, S 2017, Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery. in 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, United Kingdom, 21/06/17. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery. / Ma, YingLiang; Dagnino, Giulio; Georgilas, Ioannis; Dogramadzi, Sanja.

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery

AU - Ma, YingLiang

AU - Dagnino, Giulio

AU - Georgilas, Ioannis

AU - Dogramadzi, Sanja

PY - 2017

Y1 - 2017

N2 - Long-bone fractures such as femur fractures are very common in trauma centers. Robotic assisted fracture surgery (RAFS) can facilitate the minimally invasive surgery which reduces scarring, infection risk and long hospital stays. One important step in RAFS is to establish the coordinate system link between patient joint (rigidly connected with the robotic system) and an external tracking system. As X-ray fluoroscopic images are routinely used during the procedure, an automatic method is proposed to detect and localize landmarks on the tracking tool using live X-ray image. The proposed method uses combination of block detection, geometric model matching and principle component analysis. A successful rate of 91.3% is achieved after testing on 650 X-ray images and accuracy is within 0.5 mm.

AB - Long-bone fractures such as femur fractures are very common in trauma centers. Robotic assisted fracture surgery (RAFS) can facilitate the minimally invasive surgery which reduces scarring, infection risk and long hospital stays. One important step in RAFS is to establish the coordinate system link between patient joint (rigidly connected with the robotic system) and an external tracking system. As X-ray fluoroscopic images are routinely used during the procedure, an automatic method is proposed to detect and localize landmarks on the tracking tool using live X-ray image. The proposed method uses combination of block detection, geometric model matching and principle component analysis. A successful rate of 91.3% is achieved after testing on 650 X-ray images and accuracy is within 0.5 mm.

KW - computer vision

KW - robotics

KW - computer assisted surgery

U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

M3 - Conference contribution

BT - 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

PB - IEEE

T2 - 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

Y2 - 21 June 2017 through 23 June 2017

ER -

Ma Y, Dagnino G, Georgilas I, Dogramadzi S. Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery. In 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE. 2017 https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.136

Automatic Tool Detection in X-Ray Images for Robotic Assisted Joint Fracture Surgery

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