Probabilistic Anatomical Labeling of Brain Structures Using Statistical Probabilistic Anatomical Maps
Bibliographic record
Abstract
Purpose: The use of statistical parametric mapping (SPM) program has increased for the analysis of brain PET and SPECT images. Montreal Neurological Institute (MNI) coordinate is used in SPM program as a standard anatomical framework. While the most researchers look up Talairach atlas to report the localization of the activations detected in SPM program, there is significant disparity between MNI templates and Talairach atlas. That disparity between Talairach and MNI coordinates makes the interpretation of SPM result time consuming, subjective and inaccurate. The purpose of this study was to develop a program to provide objective anatomical information of each x-y-z position in ICBM coordinate. Materials and Methods: Program was designed to provide the anatomical information for the given x-y-z position in MNI coordinate based on the Statistical Probabilistic Anatomical Map (SPAM) images of ICBM. When x-y-z position was given to the program, names of the anatomical structures with non-zero probability and the probabilities that the given position belongs to the structures were tabulated. The program was coded using IDL and JAVA language for 4he easy transplantation to any operating system or platform. Utility of this program was shown by comparing the results of this program to those of SPM program. Preliminary validation study was peformed by applying this program to the analysis of PET brain activation study of human memory in which the anatomical information on the activated areas are previously known. Results: Real time retrieval of probabilistic information with 1 mm spatial resolution was archived using the programs. Validation study showed the relevance of this program: probability that the activated area for memory belonged to hippocampal formation was more than 80%. Conclusion: These programs will be useful for the result interpretation of the image analysis peformed on MNI coordinate, as done in SPM program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".