A Novel Approach to Detect Anatomical Landmarks Using R-CNN for MEG-MRI Registration
SSRN Electronic Journal
- 1Citations
- 219Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In a general scenario, magnetoencephalography (MEG) and magnetic resonance imaging (MRI) registration is performed by manually marking the fiducial (external) markers placed at the individual’s left pre-auricle (LPA), right pre-auricle (RPA), and nasion. The drawback of using external markers can lead to inaccurate registration due to subjective error, and registration cannot be performed on MRI, which doesn’t have markers in it. In the proposed study, we introduce a novel approach that automatically detects LPA, RPA, and nasion in MR images without using any fiducial markers. Since LPA and RPA are not anatomically appealing in MR images, we detected the neighboring structure that has a well-defined anatomical relationship with LPA and RPA, such as Temporomandibular Joint (TMJ), using a region-based Convolutional neural network (R-CNN). Using the anatomical relationship between the nasion and pre-auricular point, we detected nasion using image processing techniques. Using these three anatomical landmarks, we performed MEG-MRI registration on forty-three subjects. To validate the proposed study, we performed the localization of the N20 component derived from Somatosensory Evoked Fields (SSEF) of five subjects. The handcrafted R-CNN model was successful in detecting TMJ. The result showed that registration error (in millimeters) between proposed automated registration and manual registration is 3.6028 ± 1.4037, 4.0512 ± 1.736, and 2.7118 ± 2.7942 for LPA, RPA, and nasion, respectively. The validation results obtained for five subjects showed the localization error to be less than 1 millimeter. From the results, we can say that the proposed MEG-MRI registration can replace fiducial-based MEG-MRI registration.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know