PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.
Authors
Affiliations (13)
Affiliations (13)
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands; Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: [email protected].
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands.
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Istanbul Medipol University, Istanbul, Turkey.
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI-UMR 1099, F-35000 Rennes, France.
- Qingdao University, Qingdao, China.
- Stevens Institute of Technology, NJ, United States.
- Shanghai Jiao Tong University, Shanghái, China.
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands; Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract
Accurate delineation of pancreatic tumors on Magnetic Resonance Imaging (MRI) is important for diagnosis, radiotherapy treatment planning, and outcome assessment, but remains challenging due to complex anatomy and subtle tumor appearance. In routine practice, tumor contours on MRI are produced manually, which is time-consuming and subject to inter-observer variability. Radiotherapy on MRI-Linear Accelerator (MRI-Linac) systems further requires fast and consistent Gross Tumor Volume (GTV) contours for online adaptation, yet most public pancreas tumor segmentation benchmarks focus on Computed Tomography (CT). The Pancreatic Tumor Segmentation in Therapeutic and Diagnostic MRI (PANTHER) challenge addresses this gap by benchmarking automatic pancreatic tumor segmentation on MRI. The dataset includes contrast-enhanced T1-weighted diagnostic MRI and T2-weighted MRI-Linac scans with expert pancreas and tumor annotations, organized into two tasks: (1) tumor segmentation on diagnostic MRI and (2) tumor segmentation on MRI-Linac images. Performance was evaluated using overlap metrics, distance-based metrics, and tumor volume error. The challenge attracted 285 registered participants, with 12 and 9 final submissions for Tasks 1 and 2, respectively. On diagnostic MRI, top methods achieved performance close to inter-reader agreement. Multi-reader analysis suggested that models often reproduced the contouring style of the training annotator, highlighting the importance of annotation quality and consensus. In contrast, performance on MRI-Linac images was lower and more heterogeneous, including cases of complete localization failure. PANTHER provides the first public benchmark for pancreatic tumor segmentation on MRI, showing that clinically useful automation is feasible on diagnostic MRI, while robust MRI-Linac GTV segmentation remains an open challenge.