CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke\n Lesion Segmentation
Bibliographic record
Abstract
Infarcted brain tissue resulting from acute stroke readily shows up as\nhyperintense regions within diffusion-weighted magnetic resonance imaging\n(DWI). It has also been proposed that computed tomography perfusion (CTP) could\nalternatively be used to triage stroke patients, given improvements in speed\nand availability, as well as reduced cost. However, CTP has a lower signal to\nnoise ratio compared to MR. In this work, we investigate whether a conditional\nmapping can be learned by a generative adversarial network to map CTP inputs to\ngenerated MR DWI that more clearly delineates hyperintense regions due to\nischemic stroke. We detail the architectures of the generator and discriminator\nand describe the training process used to perform image-to-image translation\nfrom multi-modal CT perfusion maps to diffusion weighted MR outputs. We\nevaluate the results both qualitatively by visual comparison of generated MR to\nground truth, as well as quantitatively by training fully convolutional neural\nnetworks that make use of generated MR data inputs to perform ischemic stroke\nlesion segmentation. Segmentation networks trained using generated CT-to-MR\ninputs result in at least some improvement on all metrics used for evaluation,\ncompared with networks that only use CT perfusion input.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".