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arXiv · 2610.34599

An Agentic AI Workflow for Liver Lesion Segmentation and Structured Radiology Reporting

Fatma Ahmed Mansour·Rahatara Ferdousi·Manar Fathy Elshenawy
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Abstract

Despite advances in automated liver lesion segmentation, a gap remains between image-derived predictions and clinically interpretable radiology reports. Bridging this gap requires quantitative measurements that are both reproducible and traceable. We present an evidence-grounded agentic AI framework that connects liver and lesion segmentation with deterministic quantification and structured reporting. TotalSegmentator-based nnU-Net models delineate the liver, Couinaud segments, and lesions, while computational tools extract volumes, dimensions, counts, and anatomical locations. A large language model (LLM) organizes these measurements into a predefined reporting schema, with numerical consistency checks against source measurements. On 51 LiTS cases, the segmentation stage achieved a liver Dice score of 0.947, lesion sensitivity of 75.6%, and positive predictive value of 68.9%. Exploratory screening of 25,494 MERLIN reports identified 10,208 (40.0%) with liver-related abnormalities. By connecting quantitative imaging evidence with agentic report generation, this work takes a step toward human-centered medical AI that prioritizes traceability and clinical relevance.

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Fatma Ahmed Mansour, Rahatara Ferdousi, Manar Fathy Elshenawy. 2026-10-25. An Agentic AI Workflow for Liver Lesion Segmentation and Structured Radiology Reporting. https://arxiv.org/abs/2610.34599

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