Abstract PR-07: Automated segmentation pipeline for radiological imaging using UniverSeg and similarity-guided support set retrieval
Bibliographic record
Abstract
Abstract Automated segmentation is essential in medical imaging, aiding the identification of organs and pathologies for diagnosis, treatment planning, and disease monitoring. However, traditional models rely on large, manually-labeled datasets, task-specific retraining, and complex setup processes, limiting scalability in clinical practice. These challenges are particularly significant in oncology, where timely, accurate segmentation of tumors and affected organs is crucial. This project introduces a scalable, AI-driven pipeline centered around UniverSeg, a pretrained model that performs multi-structure segmentation with minimal user input. UniverSeg leverages a small support set of annotated images, eliminating the need for retraining and enabling adaptability across diverse imaging tasks.A key innovation of this pipeline is the automatic selection of an optimal support set. Searching algorithms, including hash-based methods, structural similarity index (SSIM), learned perceptual image patch similarity (LPIPS), and dense vector comparison, retrieve annotated images from a pre-built database (e.g., TotalSegmentator with 1,228 CT scans and 117 segmented structures) that closely match the input image provided by the radiologist. The retrieved support set then guides UniverSeg in accurately segmenting multiple structures within the scan.We evaluated the performance of various search methods and support set sizes (5, 10, and 15 patients). LPIPS delivered the highest segmentation accuracy for complex anatomical regions, while hash-based and dense vector methods demonstrated efficient image retrieval. Larger support sets further improved segmentation performance, particularly for intricate regions such as lungs and abdominal organs. The pipeline achieved consistent results across TotalSegmentator and BTCV datasets. This solution has significant implications for oncology workflows. Accurate segmentation of tumors and related structures is essential for assessing tumor burden, quantifying volumes, planning radiation therapy, and tracking treatment response. For example, a radiologist can upload a CT slice containing a tumor, and the pipeline will automatically segment the tumor and surrounding structures, providing critical data for precision treatment. The system also reduces variability across institutions and enhances workflow efficiency by eliminating extensive manual annotations and specialized AI requirements. By integrating UniverSeg with automated support set retrieval, this pipeline bridges the gap between AI research and clinical oncology. Its ability to streamline and scale segmentation tasks improves imaging analysis, supporting better decision-making in cancer diagnosis, prognosis, and treatment planning. Citation Format: Niket Patel, Vishwa Parekh. Automated segmentation pipeline for radiological imaging using UniverSeg and similarity-guided support set retrieval [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr PR-07.
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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 | 0.003 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".