SU‐E‐J‐87: Lung Deformable Image Registration Using Surface Mesh Deformation for Dose Distribution Combination
Bibliographic record
Abstract
Purpose: To allow a reliable deformable image registration (DIR) method for dose calculation in radiation therapy. This work proposes a performance assessment of a morphological segmentation algorithm that generates a deformation field from lung surface displacements with 4DCT datasets. Methods: From the 4DCT scans of 15 selected patients, the deep exhale phase of the breathing cycle is identified as the reference scan. Varian TPS EclipseTM is used to draw lung contours, which are given as input to the morphological segmentation algorithm. Voxelized contours are smoothed by a Gaussian filter and then transformed into a surface mesh representation. Such mesh is adapted by rigid and elastic deformations to match each subsequent lung volumes. The segmentation efficiency is assessed by comparing the segmented lung contour and the TPS contour considering two volume metrics, defined as Volumetric Overlap Error (VOE) [%] and Relative Volume Difference (RVD) [%] and three surface metrics, defined as Average Symmetric Surface Distance (ASSD) [mm], Root Mean Square Symmetric Surface Distance (RMSSD) [mm] and Maximum Symmetric Surface Distance (MSSD) [mm]. Then, the surface deformation between two breathing phases is determined by the displacement of corresponding vertices in each deformed surface. The lung surface deformation is linearly propagated in the lung volume to generate 3D deformation fields for each breathing phase. Results: The metrics were averaged over the 15 patients and calculated with the same segmentation parameters. The volume metrics obtained are a VOE of 5.2% and a RVD of 2.6%. The surface metrics computed are an ASSD of 0.5 mm, a RMSSD of 0.8 mm and a MSSD of 6.9 mm. Conclusion: This study shows that the morphological segmentation algorithm can provide an automatic method to capture an organ motion from 4DCT scans and translate it into a volume deformation grid needed by DIR method for dose distribution combination.
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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) | 0.004 | 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".