Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions

被引:2
|
作者
Pinte, Caroline [1 ]
Fleury, Mathis [1 ]
Maurel, Pierre [1 ]
机构
[1] Univ Rennes, INRIA, CNRS, INSERM,Empenn ERL,U1228, Rennes, France
来源
FRONTIERS IN NEUROLOGY | 2021年 / 12卷
关键词
EEG; fMRI; electrode detection; electrode labeling; deep learning; U-Net; ICP; POSITIONS; REGISTRATION; NETWORKS; EPILEPSY;
D O I
10.3389/fneur.2021.644278
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.
引用
收藏
页数:8
相关论文
共 50 条
  • [21] Weakly Supervised Deep Learning-based Intracranial Hemorrhage Localization
    Nemcek, Jakub
    Vicar, Tomas
    Jakubicek, Roman
    PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES (BIOIMAGING), VOL 2, 2021, : 111 - 116
  • [22] Deep Learning-based Type Identification of Volumetric MRI Sequences
    Vieira de Mello, Jean Pablo
    Paixao, Thiago M.
    Berriel, Rodrigo
    Reyes, Mauricio
    Badue, Claudine
    De Souza, Alberto F.
    Oliveira-Santos, Thiago
    2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 5674 - 5681
  • [23] Channel swapping of EEG signals for deep learning-based seizure detection
    Pan, Yayan
    Dong, Fangying
    Yao, Wei
    Meng, Xiaoqin
    Xu, Yongan
    ELECTRONICS LETTERS, 2024, 60 (14)
  • [24] Epileptic EEG classification via deep learning-based strange attractor
    Lin, Yongzheng
    Dong, Li
    Jiang, Yan
    Lian, Jian
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2025, 100
  • [25] A Deep Learning-Based Classification Method for Different Frequency EEG Data
    Wen, Tingxi
    Du, Yu
    Pan, Ting
    Huang, Chuanbo
    Zhang, Zhongnan
    Wong, Kelvin
    COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2021, 2021
  • [26] Application of polymer sensitive MRI sequence to localization of EEG electrodes
    Butler, Russell
    Gilbert, Guillaume
    Descoteaux, Maxime
    Bernier, Pierre-Michel
    Whittingstall, Kevin
    JOURNAL OF NEUROSCIENCE METHODS, 2017, 278 : 36 - 45
  • [27] DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution
    Liu, Huanyu
    Liu, Jiaqi
    Li, Junbao
    Pan, Jeng-Shyang
    Yu, Xiaqiong
    JOURNAL OF HEALTHCARE ENGINEERING, 2021, 2021
  • [28] Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks
    Liu, Runjie
    Zhang, Qionggui
    Zhang, Yuankang
    Zhang, Rui
    Meng, Tao
    SENSORS, 2024, 24 (16)
  • [29] Deep learning-based spectroscopic single-molecule localization microscopy
    Gaire, Sunil Kumar
    Daneshkhah, Ali
    Flowerday, Ethan
    Gong, Ruyi
    Frederick, Jane
    Backman, Vadim
    JOURNAL OF BIOMEDICAL OPTICS, 2024, 29 (06)
  • [30] Deep learning-based detection, classification, and localization of defects in semiconductor processes
    Patel, Dhruv, V
    Bonam, Ravi
    Oberai, Assad A.
    JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS, 2020, 19 (02):