Single Seed Region Growing Algorithm in Dynamic PET Imaging (SSRG/4D-PET) for Tumor Volume Delineation in Radiotherapy Treatment Planning: Theory and Simulation
Bibliographic record
Abstract
Tumor volume delineation plays a critical role in radiation treatment planning and simulation. Inaccurately defined target volume may lead to overdosing of normal structures surrounding the tumor, while potentially underdosing cancerous tissue. Conventional 3-dimensional tumor segmentation methods ignore the temporal information present in dynamic Positron Emission Tomography (PET) images and thus may falsely classify equal intensity voxels with completely different time activity curves (TACs) as belonging to the same tissue. We present a novel approach to tumor volume delineation in dynamic PET based on TAC differences. Principal Component Analysis, nonlinear curve-fitting, region growing, and morphological reconstruction are integrated within a single framework for this purpose. A partially-supervised approach is pursued in order to allow an expert reader to utilize the information available from other imaging modalities routinely used in conjunction with PET. In our scheme, this includes the definition of a tumor encompassing mask and selection of a seed site within the suspected tumor, while further delineation is performed by the algorithm automatically. Performance of the proposed algorithm is compared to five other methods. Simulations and a phantom study show that accurate tumor volume delineation can be achieved.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".