Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients
Bibliographic record
Abstract
Depending on the application, multiple imaging modalities are available for diagnosis in the clinical routine. As a result of this, repositories of patient scans often contain mixed modalities. This poses a challenge for image analysis methods, which require special modifications to work with multiple modalities. This is especially critical for deep learning-based methods, which require large amounts of data. Within this context, a typical example is follow-up imaging in acute ischemic stroke patients, which is an important step in determining potential complications from the evolution of a lesion. In this study, we addressed the mixed modalities issue by translating unpaired images between two of the most relevant follow-up stroke modalities, namely non-contrast computed tomography (NCCT) and fluid-attenuated inversion recovery (FLAIR) MRI. For the translation, we use the widely used cycle-consistent generative adversarial network (CycleGAN). To preserve stroke lesions after translation, we implemented and tested two modifications to regularize them: (1) we use manual segmentations of the stroke lesions as an attention channel when training the discriminator networks, and (2) we use an additional gradient-consistency loss to preserve the structural morphology. For the evaluation of the proposed method, 238 NCCT and 244 FLAIR scans from acute ischemic stroke patients were available. Our method showed a considerable improvement over the original CycleGAN. More precisely, it is capable to translate images between NCCT and FLAIR while preserving the stroke lesion’s shape, location, and modality-specific intensity (average Kullback-Leibler divergence improved from 2,365 to 396). Our proposed method has the potential of increasing the amount of available data used for existing and future applications while conserving original patient features and ground truth labels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".