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DeepNavNet: Automated Landmark Localization for Neuronavigation

Edwards, CA, Goyal, A, Rusheen, AE, Kouzani, Abbas and Lee, KH 2021, DeepNavNet: Automated Landmark Localization for Neuronavigation, Frontiers in Neuroscience, vol. 15, pp. 1-12, doi: 10.3389/fnins.2021.670287.

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Title DeepNavNet: Automated Landmark Localization for Neuronavigation
Author(s) Edwards, CA
Goyal, A
Rusheen, AE
Kouzani, AbbasORCID iD for Kouzani, Abbas orcid.org/0000-0002-6292-1214
Lee, KH
Journal name Frontiers in Neuroscience
Volume number 15
Article ID 670287
Start page 1
End page 12
Total pages 12
Publisher Frontiers Research Foundation
Place of publication Lausanne, Switzerland
Publication date 2021-06
ISSN 1662-4548
1662-453X
Keyword(s) ACCURACY
ANTERIOR
deep brain stimulation
DEEP BRAIN-STIMULATION
deep learning
human-machine teaming
IMPLANTATION
landmark localization
Life Sciences & Biomedicine
neuroimaging
neuronavigation
Neurosciences
Neurosciences & Neurology
neurosurgery planning
POSTERIOR COMMISSURES
Science & Technology
SELECTION
Summary Functional neurosurgery requires neuroimaging technologies that enable precise navigation to targeted structures. Insufficient image resolution of deep brain structures necessitates alignment to a brain atlas to indirectly locate targets within preoperative magnetic resonance imaging (MRI) scans. Indirect targeting through atlas-image registration is innately imprecise, increases preoperative planning time, and requires manual identification of anterior and posterior commissure (AC and PC) reference landmarks which is subject to human error. As such, we created a deep learning-based pipeline that consistently and automatically locates, with submillimeter accuracy, the AC and PC anatomical landmarks within MRI volumes without the need for an atlas. Our novel deep learning pipeline (DeepNavNet) regresses from MRI scans to heatmap volumes centered on AC and PC anatomical landmarks to extract their three-dimensional coordinates with submillimeter accuracy. We collated and manually labeled the location of AC and PC points in 1128 publicly available MRI volumes used for training, validation, and inference experiments. Instantiations of our DeepNavNet architecture, as well as a baseline model for reference, were evaluated based on the average 3D localization errors for the AC and PC points across 311 MRI volumes. Our DeepNavNet model significantly outperformed a baseline and achieved a mean 3D localization error of 0.79 ± 0.33 mm and 0.78 ± 0.33 mm between the ground truth and the detected AC and PC points, respectively. In conclusion, the DeepNavNet model pipeline provides submillimeter accuracy for localizing AC and PC anatomical landmarks in MRI volumes, enabling improved surgical efficiency and accuracy.
Language eng
DOI 10.3389/fnins.2021.670287
Indigenous content off
Field of Research 1109 Neurosciences
1701 Psychology
1702 Cognitive Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30153214

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.