Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors developed TRIAGE, a tool to help doctors mark tumors in full-body PET/CT scans more easily and accurately. Their system first learns general anatomy and differences in scan types (FDG vs. PSMA) using advanced training methods. It then uses organ information and a special classifier to choose the right model branch for the scan type, producing an initial tumor map. Doctors can interact by adding quick corrections, and the system updates the tumor map in response. The method was tested on standard datasets, with detailed results to follow after official evaluations.
PET/CT imaginglesion segmentationFDG tracerPSMA tracer3D U-Netmasked autoencodingorgan segmentationinteractive segmentationcurriculum trainingmodel ensembling
Authors
Xinglong Liang, Chunyao Lu, Tianyu Zhang, Jiaju Huang, Tao Tan, Yunchao Yin, Lishan Cai
Abstract
Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to sparse corrective scribbles during inference. This setting is particularly challenging because tracer distributions, physiological uptake patterns, lesion appearance, and acquisition characteristics differ substantially between FDG and PSMA studies. We present TRIAGE, Tracer-aware Refinement via Interactive Anatomy-Guided sEgmentation. The core backbone is a 3D STU-Net initialized through masked autoencoding pre-training with an asynchronous masking strategy, aiming to learn transferable anatomical and cross-modal representations before task-specific fine-tuning. In parallel, we train an auxiliary organ segmentation model whose predictions provide explicit anatomical context and help distinguish physiological uptake from malignant lesions. A dedicated tracer classifier first routes each study to an FDG- or PSMA-specific branch. Within each branch, a first-stage segmentation model consumes CT, PET, and organ context to generate an initial lesion mask. The initial prediction is then combined with cumulative foreground/background scribbles and refined by a second interactive segmentation network. The FDG and PSMA branches share the same overall processing pipeline but are trained independently to account for tracer-specific appearance and error modes. We additionally employ curriculum-style training and model ensembling to improve robustness across interaction steps and heterogeneous cohorts. Experiments are conducted using the official AutoPET V data and ten-fold split; quantitative results, ablations, and final test-set performance are left as placeholders to be completed after the challenge evaluation. Code: https://github.com/Liiiii2101/AUTOPET2026-MEDAI.