Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning
Bibliographic record
Abstract
Abstract Background and Purpose : This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning. Materials and Methods . The evaluation was conducted on a private clinical dataset and a publicly available dataset (HaN-Seg). Anonymized MRI data from 55 brain cancer patients, including T1-weighted, T1-weighted with contrast, and T2-weighted images, were used in the clinical dataset. We employed an EDL strategy that integrated five independently trained 2D neural networks, each tailored for precise segmentation of tumors and organs at risk (OARs) in the MRI scans. Class probabilities were obtained by averaging the final layer activations (Softmax outputs) from the five networks using a weighted-average method, which were then converted into discrete labels. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance at 95% (HD95). The EDL model was also tested on the HaN-Seg public dataset for comparison. Results . The EDL model demonstrated superior segmentation performance on both the clinical and public datasets. For the clinical dataset, the ensemble approach achieved an average DSC of 0.7 ± 0.2 and HD95 of 4.5 ± 2.5 mm across all segmentations, significantly outperforming individual networks which yielded DSC values ≤0.6 and HD95 values ≥14 mm. Similar improvements were observed in the HaN-Seg public dataset. Conclusions . Our study shows that the EDL model consistently outperforms individual CNN networks in both clinical and public datasets, demonstrating the potential of ensemble learning to enhance segmentation accuracy. These findings underscore the value of the EDL approach for clinical applications, particularly in MRI-guided RT 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 | 0.000 | 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 | 0.001 |
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".