High-Precision, Automatic, and Fast Segmentation Method of Hepatic Vessels and Liver Tumors from CT Images Using a Fusion Decision-Based Stacking Deep Learning Model
Bibliographic record
Abstract
Background: To propose an automatic liver and hepatic vessel segmentation solution based on a stacking model and decision fusion. This model combines the decisions of multiple models to achieve increased accuracy. It exhibits improved robustness due to the reduction of individual errors. Flexibility is also a key asset, with combination methods such as majority voting or weighted averaging. The model enables managing the uncertainty associated with individual decisions to obtain a more reliable final decision. The combination of decisions improves the overall accuracy of the system. Methods: This research introduces a new deep learning-based architecture for automatically segmenting hepatic vessels and tumors from CT scans, utilizing stacking, decision fusion, and deep transfer learning to achieve high-accuracy and rapid segmentation. This study employed two distinct datasets: the external “Medical Segmentation Decathlon (MSD) task 08” dataset and an internal dataset procured from Ibn Sina University Hospital encompassing a cohort of 112 patients with chronic liver disease who underwent contrast-enhanced abdominal CT scans. Results: The proposed segmentation model reached a DSC of 83.21 and an IoU of 72.76 for hepatic vasculature and tumor segmentation, thereby exceeding the performance benchmarks established by the majority of antecedent studies. Conclusions: This study introduces an automated method for liver vessels and liver tumor segmentation, combining precision and stability to bridge the clinical gap. Furthermore, decision fusion-based stacking models have a significant impact on clinical applications by enhancing diagnostic accuracy, enabling personalized care through the integration of genetic, environmental, and clinical data, optimizing clinical trials, and facilitating the development of personalized medicines and therapies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".