Creating A 3D Histological Atlas of Subcortical Nuclei using Ultra‐High Field MRI Registration: A Model for DBS Surgical Planning
Bibliographic record
Abstract
Introduction/Objectives Deep Brain Stimulation (DBS) is an effective neurosurgical intervention that is used for the treatment of Parkinson's Disease (PD) and other movement disorders. Through the development of higher resolution templates and atlases, DBS targeting can be performed with higher accuracy. The aim of this study is to create a histological atlas of the subcortex and evaluate its ability to inform DBS surgical planning. Methods The histological template was creating through in‐situ 7 Tesla (T) Magnetic Resonance Imaging (MRI) of a cadaveric brain, followed by ex‐vivo 7T MR imaging and histological processing of the subcortex. Histology‐to‐MRI registration allowed for the mapping and reconstruction of histological sections back into the MRI space. Ten subcortical structures were directly visualized and segmented based on MRI and histology data. Results Through direct visualization of subcortical nuclei we demonstrated that these anatomical structures can be segmented with higher accuracy using histological data as opposed to 7T MRI data. Conclusion We hope that further development of this model can be applied to clinically significant anatomical structures, such as the Subthalamic Nucleus and Globus Pallidus Interna, when planning DBS surgery for PD patients. Support or Funding Information Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council, BrainsCAN, Brain Canada Foundation This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".