P2‐028: Influence of the training library composition on a patch‐based label fusion method: Application to hippocampus segmentation on the ADNI dataset
Bibliographic record
Abstract
The atrophy of medial temporal lobe structures may serve as early biomarkers of Alzheimer's disease. The evaluation of hippocampus (HC) atrophy is estimated by volumetric studies requiring a segmentation step that can be very time consuming when done manually. This limitation can be overcome by using automatic segmentation methods. In this study, we propose to validate our nonlocal patch-based method [1] on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database by segmenting the HC of Cognitively Normal (CN) subjects and patients with early Alzheimer's disease (AD). We used images obtained from the ADNI database. For our experiments, we randomly selected 10 MRI of CN and 10 MRI of AD. The first experiment was designed to evaluate the accuracy of our segmentation method on a group constituting of CN and AD (8 CN and 8 AD). The second experiment was designed to investigate the impact of the composition of the training library. Three different training populations were built (i.e., the CN population: 8 CN, the AD population: 8 AD, and the mixed AD/CN population: 4 CN and 4 AD). Through a leave-one-out procedure, our automatic segmentation method was applied on each of the 20 MRI scans. The quality of the obtained automatic segmentations was evaluated by estimating the Dice Kappa similarity index. For the first experiment, the median Dice Kappa values are presented in Table 1. The segmentation accuracy was significantly better (p-value = 0.002) for CN (median k = 0.883 for both HC) than for AD (median k = 0.838 for both HC). For the second experiment, Figure 1 and Table 2 show the Dice Kappa similarity index for both studied populations according to the training library composition. For both populations, the best median Kappa values were obtained with the mixed CN/AD training library (k = 0.875 for CN and k = 0.835 AD). First, we demonstrated that our patch-based method provides high segmentation accuracy for both CN and AD populations. Second, we showed that the characteristic of the training library has a significant impact on the segmentation accuracy. 1. Coupe, P., et al., Neuroimage, 2011. 54(2): p. 940-54. Kappa index distribution according to the composition of the training library for both studied populations.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".