Localization of perfusion abnormalities in brain SPECT imaging (Honorable Mention Poster Award)
Bibliographic record
Abstract
SPECT (Single Photon Emission Computed Tomography) imagery has become widely available and is particularly useful for regional cerebral blood flow (rCBF) studies. Distribution of rCBF is still essentially studied by visual observation, searching for abnormalities, and comparing with other studies. In order to facilitate the localization of these abnormalities, we propose a simple, automatic and direct method to register a SPECT rCBF study with a commonly used atlas in the neurological community, the Talairach Atlas. The Talairach atlas still gives today the most extensive information of regions of interests, coupled with a coordinate system. The proposed method will therefore allow a physician to precisely navigate in a SPECT image by interpreting the abnormalities coordinates. The registration of these two volumes is carried out in two steps, a rough alignment followed by an elastic registration. The rough alignment step consists in computing the mass centroid of each volume and in scaling the volumes accordingly if necessary. A simple threshold method (30% of the maximum intensity of the SPECT image) is used to determine the volume of the brain being studied. In order to facilitate the fine registration, the Talairach atlas was previously segmented in three classes: cerebrospinal fluid (CSF), white and gray matters. Then, an automatic intensity transformation as well as a low-pass filtering is performed to closely resemble the spatial resolution and intensities of the SPECT volume. This intensity transformation is a simple method which combines the use of a joint 2D histogram of the segmented atlas and the individual volume as well as a clustering algorithm. The fine registration is then computed with an optical flow methodology. The effectiveness of this scheme was tested on a database of virtual patients, simulated from a database of 45 healthy and diseased brains. The rate of pixels misclassification in each class within a one pixel neighborhood (CSF 0.5%; white matter 1.37%, gray matter 2.80%) indicates that this proposed method will be useful for the nuclear physician in helping localize abnormalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.165 | 0.061 |
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".