Uncertainty-guided and cross-modality attention network for liver tumor segmentation and quantification via integrating dynamic MRI
Bibliographic record
Abstract
Segmentation and quantitative measurement of liver tumors, including hemangiomas and hepatocellular carcinoma (HCC), using dynamic Magnetic Resonance Imaging (MRI) sequences are crucial for effective treatment and prognosis. However, these tasks remain challenging due to two key issues: (1) the severe class imbalance between tumors and background, particularly for small HCC lesions, which complicates precise feature extraction; and (2) the diverse imaging features across dynamic MRI phases, making effective fusion of multi-phase information difficult. To address these challenges, this study proposes the Uncertainty-guided and Cross-modality Attention Network (UgCmA-Net). UgCmA-Net incorporates three innovative components: (1) a cross-modality attention pyramid module within a parallel attention-based encoder, enhancing tumor-specific feature extraction across dynamic phases; (2) a fusion Transformer (F-Trans), where the non-local Transformer captures long-range dependencies, and the phase-aware Transformer fuses multi-phase dynamic MRI features; and (3) an uncertainty-guided auxiliary-primary segmentor, which improves edge confidence and segmentation accuracy through uncertainty estimation. The UgCmA-Net was validated using dynamic MRI sequences (T1 pre-contrast MRI, arterial-phase, portal venous phase, and delay-phase contrast-enhanced MRI) from 265 clinical subjects. Experimental results show that the proposed UgCmA-Net achieves state-of-the-art performance, with a dice similarity coefficient of 85.44%, Hausdorff Distance of 2.28 mm, and mean absolute error values of 1.85 mm, 1.90 mm, 6.52 mm, and 97.27 mm 2 for multi-index quantification of center point, max-diameter, circumference, and area, respectively. Statistical analysis confirms that the improvements are statistically significant (p < 0.05), demonstrating the robustness of the proposed method. These findings demonstrate that UgCmA-Net is highly effective for liver tumor segmentation and quantification, indicating its potential clinical value in liver tumor analysis and treatment planning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".