TY - GEN
T1 - Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors
AU - Schlesinger, Oded
AU - Kundu, Raj
AU - Goetz, Stefan
AU - Sapiro, Guillermo
AU - Peterchev, Angel V.
AU - Di Martino, J. Matias
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10–20 System’s neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.
AB - The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10–20 System’s neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.
KW - Automatic landmark detection
KW - Supervised learning
KW - TMS
UR - http://www.scopus.com/inward/record.url?scp=85175796086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175796086&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-45249-9_2
DO - 10.1007/978-3-031-45249-9_2
M3 - Conference contribution
C2 - 38155840
AN - SCOPUS:85175796086
SN - 9783031452482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 12
EP - 20
BT - Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging - 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023, Proceedings
A2 - Wesarg, Stefan
A2 - Oyarzun Laura, Cristina
A2 - Puyol Antón, Esther
A2 - King, Andrew P.
A2 - Baxter, John S.H.
A2 - Erdt, Marius
A2 - Drechsler, Klaus
A2 - Freiman, Moti
A2 - Chen, Yufei
A2 - Rekik, Islem
A2 - Eagleson, Roy
A2 - Feragen, Aasa
A2 - Cheplygina, Veronika
A2 - Ganz-Benjaminsen, Melani
A2 - Ferrante, Enzo
A2 - Glocker, Ben
A2 - Moyer, Daniel
A2 - Petersen, Eikel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Workshop on Clinical Image-Based Procedures, CLIP 2023, 1st MICCAI Workshop on Fairness of AI in Medical Imaging, FAIMI 2023, held in conjunction with MICCAI 2023 and 2nd MICCAI Workshop on the Ethical and Philosophical Issues in Medical Imaging, EPIMI 2023
Y2 - 12 October 2023 through 12 October 2023
ER -