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arXiv Computer Vision By Pablo Lozano-Jimenez, Sergio Romero-Tapiador, Ruben Tolosana

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

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The paper introduces an anatomy-aware, promptable segmentation model for whole-body lesion detection in FDG and PSMA PET/CT scans, tailored for the AUTOPET V challenge. The approach builds on nnU-Net, employing a two-stage training process: an initial pre-training phase for strong baseline segmentation and an online interactive phase that refines predictions using scribble prompts. Anatomical context is integrated via organ supervision with a shared head predicting both lesions and organs, reducing false positives, while a tracer classifier directs studies to either a combined FDG+PSMA model or a PSMA-specific model. Cross-validation results show that organ-supervised training yields the most stable performance, the interactive stage consistently improves Dice scores, and PSMA-specific training delivers the best tracer-wise results.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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