Hippocampus Segmentation of Brain MRI Images for Possible Progression Detection of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) has become as one of the most serious ailments that need to be faced by people all over the world. There is no cure for AD, but the progression of the illness may be reduced with the early detection and proper monitoring of the disease. Many current studies focused more on early detection but does not include the monitoring process as well. Proper monitoring system is as critical as early diagnosis since it allows doctors to assess the disease development of Alzheimer's patients quantitatively. This study proposes to develop an algorithm for detecting the hippocampus of patients with Alzheimer's disease in MRI images and use that for the purpose of progression detection of the disease. After performing some pre-processing steps, the active contour method (Chan-Vese) was used to extract the region of interest (ROI). Next, certain parameters were calculated including the number of pixels and area pixels. From the extracted parameters of the patients from two different MRI sessions, percentage of progression is calculated by measuring the reduction in pixel numbers from those images. This study was able to develop a semi-automated and robust model based on the Chan-Vese segmentation technique, where it could observe the shrinking of the patient brain by the progression method using the total pixels of the hippocampus and its area by getting decreased at the second visit. Based on the result, it shows that this study could be further extended by implementing various feature extraction techniques to come out with a more robust output which can be used for progression detection of Alzheimer’s disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".