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Multi-agent shape models for hip landmark detection in MR scans. / Bekkouch, Imad Eddine Ibrahim; Aidinovich, Tamerlan; Vrtovec, Tomaz; Kuleev, Ramil; Ibragimov, Bulat.
Medical Imaging 2021: Image Processing. red. / Ivana Isgum; Bennett A. Landman. SPIE - International Society for Optical Engineering, 2021. 115960O (Proceedings of S P I E - International Society for Optical Engineering, Bind 11596).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Bekkouch, IEI, Aidinovich, T, Vrtovec, T, Kuleev, R
& Ibragimov, B 2021,
Multi-agent shape models for hip landmark detection in MR scans. i I Isgum & BA Landman (red),
Medical Imaging 2021: Image Processing., 115960O, SPIE - International Society for Optical Engineering, Proceedings of S P I E - International Society for Optical Engineering, bind 11596, SPIE Medical Imaging 2021, Virtual, Online, USA,
15/02/2021.
https://doi.org/10.1117/12.2580862APA
Bekkouch, I. E. I., Aidinovich, T., Vrtovec, T., Kuleev, R.
, & Ibragimov, B. (2021).
Multi-agent shape models for hip landmark detection in MR scans. I I. Isgum, & B. A. Landman (red.),
Medical Imaging 2021: Image Processing [115960O] SPIE - International Society for Optical Engineering. Proceedings of S P I E - International Society for Optical Engineering Bind 11596
https://doi.org/10.1117/12.2580862Vancouver
Bekkouch IEI, Aidinovich T, Vrtovec T, Kuleev R
, Ibragimov B.
Multi-agent shape models for hip landmark detection in MR scans. I Isgum I, Landman BA, red., Medical Imaging 2021: Image Processing. SPIE - International Society for Optical Engineering. 2021. 115960O. (Proceedings of S P I E - International Society for Optical Engineering, Bind 11596).
https://doi.org/10.1117/12.2580862Author
Bekkouch, Imad Eddine Ibrahim ; Aidinovich, Tamerlan ; Vrtovec, Tomaz ; Kuleev, Ramil ; Ibragimov, Bulat. / Multi-agent shape models for hip landmark detection in MR scans. Medical Imaging 2021: Image Processing. red. / Ivana Isgum ; Bennett A. Landman. SPIE - International Society for Optical Engineering, 2021. (Proceedings of S P I E - International Society for Optical Engineering, Bind 11596).
Bibtex
@inproceedings{32017f5f122a4b9c9965db133005d046,
title = "Multi-agent shape models for hip landmark detection in MR scans",
abstract = "Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis morphometry. Hence, we propose a Deep Learning based method for automatic landmark detection on multi-modality hips magnetic resonance (MR) scans. Our method is based on a synergistic analysis of appearance and shape information by using deep networks for the detection of landmark candidate locations and then adjusting these locations using inter-landmark spatial properties. Our best model gives an average of 1.74 mm over all the landmarks, where 67% of the proposed landmarks are within the spatial matching error of at most 2mm. ",
keywords = "Deep Learning, Landmark Detection, Medical image processing",
author = "Bekkouch, {Imad Eddine Ibrahim} and Tamerlan Aidinovich and Tomaz Vrtovec and Ramil Kuleev and Bulat Ibragimov",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; SPIE Medical Imaging 2021 ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2580862",
language = "English",
series = "Proceedings of S P I E - International Society for Optical Engineering",
publisher = "SPIE - International Society for Optical Engineering",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2021",
}
RIS
TY - GEN
T1 - Multi-agent shape models for hip landmark detection in MR scans
AU - Bekkouch, Imad Eddine Ibrahim
AU - Aidinovich, Tamerlan
AU - Vrtovec, Tomaz
AU - Kuleev, Ramil
AU - Ibragimov, Bulat
N1 - Publisher Copyright:
© 2021 SPIE.
PY - 2021
Y1 - 2021
N2 - Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis morphometry. Hence, we propose a Deep Learning based method for automatic landmark detection on multi-modality hips magnetic resonance (MR) scans. Our method is based on a synergistic analysis of appearance and shape information by using deep networks for the detection of landmark candidate locations and then adjusting these locations using inter-landmark spatial properties. Our best model gives an average of 1.74 mm over all the landmarks, where 67% of the proposed landmarks are within the spatial matching error of at most 2mm.
AB - Landmark detection is an essential step in the diagnosis of bone pathologies and pelvis morphometry. Hence, we propose a Deep Learning based method for automatic landmark detection on multi-modality hips magnetic resonance (MR) scans. Our method is based on a synergistic analysis of appearance and shape information by using deep networks for the detection of landmark candidate locations and then adjusting these locations using inter-landmark spatial properties. Our best model gives an average of 1.74 mm over all the landmarks, where 67% of the proposed landmarks are within the spatial matching error of at most 2mm.
KW - Deep Learning
KW - Landmark Detection
KW - Medical image processing
U2 - 10.1117/12.2580862
DO - 10.1117/12.2580862
M3 - Article in proceedings
AN - SCOPUS:85103407228
T3 - Proceedings of S P I E - International Society for Optical Engineering
BT - Medical Imaging 2021
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
PB - SPIE - International Society for Optical Engineering
T2 - SPIE Medical Imaging 2021
Y2 - 15 February 2021 through 19 February 2021
ER -
