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AUTOPET V challenge model uses anatomy-aware segmentation for PET/CT scans

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an anatomy-aware, promptable segmentation model for whole-body lesion detection in PET/CT scans, specifically for the AUTOPET V challenge. The model, built on the nnU-Net framework, utilizes a two-stage training process: initial pre-training for strong segmentation and an online interactive stage that refines predictions using scribble prompts. By incorporating organ supervision and a tracer classification system, the model aims to reduce false positives and improve accuracy across different tracers like fludeoxyglucose (18F) and glutamate carboxypeptidase II (PSMA). AI

IMPACT This research advances AI capabilities in medical imaging, potentially improving diagnostic accuracy and efficiency for lesion detection in PET/CT scans.

RANK_REASON The item is an academic paper detailing a new method for biomedical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AUTOPET V challenge model uses anatomy-aware segmentation for PET/CT scans

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The item is an academic paper detailing a new method for biomedical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Pablo Lozano-Jimenez, Sergio Romero-Tapiador, Ruben Tolosana ·

    Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V

    arXiv:2608.28461v1 Announce Type: new Abstract: We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The proposed method is built as family of nnU-Net-based models and trained in two stages…

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