PET/CT Tumor Segmentation

CT: Computed Tomography · PET: Positron Emission Tomography

PET/CT tumor segmentation identifies and outlines lesions by combining PET signals of metabolic activity with CT anatomy, supporting disease assessment and quantitative measurement of tumor burden. Common approaches include 3D U-Net models such as nnU-Net, PET/CT foundation models, and self-supervised or semi-supervised training to make better use of limited annotations. AutoPET-style benchmarks evaluate lesion overlap, missed tumor volume, and false positives, while interactive methods use scribbles or anatomical context to correct errors such as mistaking normal tracer uptake for cancer. A central challenge is maintaining reliable performance across tracers, institutions, and cancer types while balancing recovery of small lesions against false-positive predictions.

Latest papers

16 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

A Unified Vision-Language Model for PSMA PET/CT Report Generation, Visual Question Answering, and Lesion Segmentation
Sep 14, 2026Yang Xing, Jiong Wu, Savas Ozdemir +11Radiology Report Generation Medical VQA
1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA · Department of Radiology, University of Florida, Jacksonville, FL, USA · Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA +3
BS: Take the Hint - Interactive Multitracer PET/CT Lesion Segmentation with a Scribble-Conditioned ResEnc U-Net
Sep 1, 2026Marven Sherif, Amgad Elmasry, Youssef Ghazal +1Interactive Medical Image Segmentation U-Net
Brightskies
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
Aug 31, 2026Xinglong Liang, Chunyao Lu, Tianyu Zhang +4Interactive Medical Image Segmentation PET/CT Imaging
Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, the Netherlands · Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands · Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao Special Administrative Region of China +2
Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
Aug 28, 2026Pablo Lozano-Jimenez, Sergio Romero-Tapiador, Ruben TolosanaMedical Image Segmentation PET/CT Tumor Segmentation
University of Amsterdam, Faculty of Science, Amsterdam, The Netherlands · BiometricsAI, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challenge
Aug 22, 2026Libo Zhang, Yue NingInteractive Medical Image Segmentation Medical Image Segmentation
Duke University
MUST-PET: MUltimodal Self-supervised learning across Tracers for whole-body PET/CT-based lesion segmentation
Aug 20, 2026Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle +3Multimodal SSL PET/CT Tumor Segmentation
Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Radiation Oncology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA
When Two Tracers Disagree: An Investigation of Multimodal Fusion for Clinical PET/CT Segmentation
Aug 19, 2026Jack A. Johnson, Bartłomiej W. PapieżPET/CT Tumor Segmentation Cross-Modality Medical Image Segmentation
Nuffield Department of Medicine, University of Oxford, UK · Department of Oncology, University of Oxford, UK · Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets
Aug 11, 2026Biratal Raj Wagle, Bashirul Azam Biswas, Grant Chau +5Data Selection Medical Image Segmentation
Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Department of Urology Yale University New Haven, CT, USA +1
HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT
Jul 29, 2026Kai Wang, Meixu Chen, Elie Nasr +2Cancer Survival Prediction PET/CT Imaging
Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for $^{18}$ F-FDG-PET/CT
Jul 4, 2026Maksym Fritsak, Maximilian Rokuss, Hubert S. Gabryś +10Cross-Modality Medical Image Segmentation PET/CT Imaging
Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland · Faculty of Medicine, University of Zurich, Zurich, Switzerland · Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany +4
RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning
Jun 23, 2026Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat +1PET/CT Imaging Medical Image Segmentation
Division of Hematology, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA · Department of Radiology, Mahidol University, Bangkok, Thailand · Department of Radiology, Mettapracharak Hospital, Thailand +1
Mutual Distillation of Dual-Foundation Models for Semi-Supervised PET/CT Segmentation
Jun 14, 2026Fuyou Mao, Beining Wu, Yanfeng Jiang +5Cross-Modality Medical Image Segmentation Medical Imaging Foundation Models
Central South University, Changsha 410083, China · Hangzhou Dianzi University, Hangzhou 310018, China · Communication University of Zhejiang, Hangzhou 310018, China +1
Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models
Jun 8, 2026Bashirul Azam Biswas, Biratal Raj Wagle, Zhihan Yang +4Ensemble Learning PET/CT Imaging
Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Radiation Oncology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA
An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation
May 20, 2026Xiaofeng Liu, Qianru Zhang, Thibault Marin +4Cross-Modality Medical Image Segmentation Medical Imaging Foundation Models
Department of Radiology and Biomedical Imaging, Yale Biomedical Imaging Institute, Yale University, New Haven, CT, USA
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
May 7, 2026Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27Domain Generalization PET/CT Imaging
Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Comprehensive Pneumology Center (CPC-M), Member of the German Center for Lung Research (DZL), Munich, Germany +13
Advanced Tumor Segmentation in PET/CT Imaging: A Training Strategy Study with nnU-Net for AutoPET III
May 4, 2026Hussain AlasmawiPET/CT Imaging Medical Image Segmentation
Mohamed bin Zayed University of Artificial Intelligence

Sep 14, 2026J/K move · Enter open · S save

A Unified Vision-Language Model for PSMA PET/CT Report Generation, Visual Question Answering, and Lesion Segmentation

Yang Xing, Jiong Wu, Savas Ozdemir, Yang Zhou+10

1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA · Department of Radiology, University of Florida, Jacksonville, FL, USA · Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA+3

Radiology Report Generation Medical VQA PET/CT Imaging Medical VLMs PET/CT Tumor Segmentation

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Accurate PSMA PET/CT interpretation is central to prostate cancer management, yet existing PET/CT AI models typically address isolated tasks. We propose a unified PSMA PET/CT vision-language model for report generation, visual question answering, and lesion segmentation. The framework adopts an LLaVA-style architecture, comprising a PET/CT vision encoder, an MLP-Mixer projection module, a LoRA-tuned large language model, and a 3D segmentation branch. Training followed a four-stage strategy: vision encoder pretraining, projection-layer alignment, VLM fine-tuning, and final multitask tuning. Language tasks used 5,747 PSMA PET/CT datasets with paired reports, while segmentation used the PSMA subset of AutoPET. The model outperformed PET2REP and a CT-based baseline across standard report-generation metrics, improved performance across VQA question types, and achieved higher Dice and lesion-level overlap F1 than SegAnyPET and nnUNet. These results support the feasibility of a unified framework for structured, interactive, interpretable PSMA PET/CT analysis with voxel-level grounding within a single multitask model architecture.