RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
RADIANT-PET is a novel reasoning-augmented framework that couples a high-sensitivity voxel-level segmentation model with lesion-level LLM adjudication for accurate lesion segmentation in PET/CT. By converting candidate regions into structured textual descriptions and optionally leveraging radiology reports, the LLM classifies lesions vs. false positives. Reinforcement learning with GRPO optimizes the LLM for correct classification and anatomically concordant site assignment. Evaluated on AutoPET and an OSU cohort, it outperforms image-only baselines, with largest gains when radiology reports are available.
[2606.28392] RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
[Submitted on 23 Jun 2026]
Title:RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
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Abstract:Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal. We present RADIANT-PET, a reasoning-augmented framework that couples a high-sensitivity voxel-level segmentation model with lesion-level large language model (LLM) adjudication. Candidate uptake regions are generated with a deliberately permissive segmentation stage, then converted into structured textual descriptions that summarize uptake intensity, morphology, and regional and global anatomical context. An LLM classifies each candidate as true lesion vs. false positive, optionally leveraging the radiology report as additional clinical context. To strengthen lesion-level reasoning, we further optimize a local LLM via reinforcement learning using Group Relative Policy Optimization, rewarding correct lesion classification and anatomically concordant site assignment. Across AutoPET and an OSU test cohort, RADIANT-PET consistently outperforms strong image-only baselines, with the largest improvements observed when radiology reports are provided. Overall, these results demonstrate that LLM-based lesion-level reasoning adds a novel reasoning layer beyond conventional segmentation, suppressing physiologic false positives and aligning voxel-level predictions with clinical interpretation. The project repository is available at: this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.28392 [cs.CV]
(or arXiv:2606.28392v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.28392
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Simeng Zhu [view email] [v1] Tue, 23 Jun 2026 18:05:55 UTC (408 KB)
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