PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging
Year: 2026
Type: article
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.
Source: Medical Image Analysis
Authors: Amparo S. Betancourt Tarifa, Marcel Verheij, René Monshouwer, Hanne D. Heerkens, Faisal Mahmood, Uffe Bernchou, Emilie Helgesen Karlsson, Ömer Faruk Durugöl, Maximilian Rokuss, Yannick Kirchhoff, Cédric Hémon, Valentin Boussot, Jean-Claude Nunes, Jean-Louis Dillenseger, Chenyuan Bian, Libo Zhang, Yue Ning, Chuanyi Huang, Lisheng Wang, Kyriaki Kolpetinou, George K. Matsopoulos, John J. Hermans, Erik van der Bijl, Peter J. Koopmans
Institutions: Radboud University Medical Center, University of Southern Denmark, Odense University Hospital, German Cancer Research Center, Istanbul Medipol University, Heidelberg University, Inserm, Laboratoire Traitement du Signal et de l'Image, Université de Rennes, Qingdao University, Stevens Institute of Technology, Shanghai Jiao Tong University, National Technical University of Athens
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Funder: Elekta
Award: G1234609457
OpenAlex ID: W7165621549