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GTV segmentation in MRI guided radiotherapy with promptable foundation models

Objective. Magnetic resonance imaging (MRI) guided radiotherapy requires the delineation of gross tumor volumes (GTV) in daily MRI from MRI-linacs. Specialized models have been developed for this task for certain tumors. This study investigated an alternative, using promptable foundation models. App...

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Autori principali: Blöcker, Tom Julius (Autore) , Delopoulos, Nikolaos (Autore) , Palacios, Miguel A (Autore) , Klüter, Sebastian (Autore) , Hörner-Rieber, Juliane (Autore) , Rippke, Carolin (Autore) , Placidi, Lorenzo (Autore) , Boldrini, Luca (Autore) , Frascino, Vincenzo (Autore) , Andratschke, Nicolaus (Autore) , Baumgartl, Michael (Autore) , Dal Bello, Riccardo (Autore) , Marschner, Sebastian N (Autore) , Belka, Claus (Autore) , Corradini, Stefanie (Autore) , Dudas, Denis (Autore) , Riboldi, Marco (Autore) , Kurz, Christopher (Autore) , Landry, Guillaume (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 29 December 2025
In: Physics in medicine and biology
Year: 2026, Volume: 71, Fascicolo: 1, Pages: 1-16
ISSN:1361-6560
DOI:10.1088/1361-6560/ae2db9
Accesso online:Verlag, kostenfrei, Volltext: https://doi.org/10.1088/1361-6560/ae2db9
Verlag, kostenfrei, Volltext: https://iopscience.iop.org/article/10.1088/1361-6560/ae2db9
Testo
Note sull'autore:Tom Julius Blöcker, Nikolaos Delopoulos, Miguel A Palacios, Sebastian Klüter, Juliane Hörner-Rieber, Carolin Rippke, Lorenzo Placidi, Luca Boldrini, Vincenzo Frascino, Nicolaus Andratschke, Michael Baumgartl, Riccardo Dal Bello, Sebastian N Marschner, Claus Belka, Stefanie Corradini, Denis Dudas, Marco Riboldi, Christopher Kurz and Guillaume Landry
Descrizione
Riassunto:Objective. Magnetic resonance imaging (MRI) guided radiotherapy requires the delineation of gross tumor volumes (GTV) in daily MRI from MRI-linacs. Specialized models have been developed for this task for certain tumors. This study investigated an alternative, using promptable foundation models. Approach. Promptable foundation models were prompted with six different sparse geometric prompt types (points, boxes, 2D masks) to produce GTV segmentation masks, including Segment-anything 2 (SAM2), SAM2 fine-tuned for medical imaging (MedSAM2), and nnInteractive, an nnUnet-based promptable model for medical imaging. A diverse multi-institutional dataset of clinical GTV masks from the abdomen, lung, liver, pancreas, and pelvis sites on MRI scans from MRI-linacs was used to evaluate model outputs using various metrics, including the Dice similarity coefficient (DSC). Main results. The models produced segmentation masks comparable or superior to those from domain-specific models with median DSCs of up to 0.85 (nnInteractive-mask3 prompt). Prompts with more spatial information yielded better results with lower variance, with the effect reduced for nnInteractive and MedSAM2. These produced overall better results (median DSC over all prompt types 0.75 for nnInteractive, 0.70 for MedSAM2, 0.54 for SAM2). Significance. This investigation showed that promptable foundation models can in principle be used for GTV segmentation in MRI across multiple tumor types, although more research is necessary to reduce the variance and improve model performance.
Descrizione del documento:Gesehen am 31.03.2026
Descrizione fisica:Online Resource
ISSN:1361-6560
DOI:10.1088/1361-6560/ae2db9

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