MRgRT Real-Time Target Localization Using Foundation Models for Contour Point Tracking and Promptable Mask Refinement

Tom Bloecker,Elia Lombardo,Sebastian N. Marschner,Claus Belka,Stefanie Corradini,Miguel A. Palacios,Marco Riboldi,Christopher Kurz,Guillaume Landry

PHYSICS IN MEDICINE AND BIOLOGY（2025）

Ludwig Maximilians Univ Munchen | Department of Radiation Oncology | Ludwig Maximilians Univ LMU

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Abstract

Objective. This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence models. Approach. The first approach used a point-tracking model that propagates points from a reference contour. The second approach used a video-object-segmentation model, based on segment anything model 2 (SAM2). Both approaches were evaluated and compared against each other, inter-observer variability, and a transformer-based image registration model, TransMorph, with and without patient-specific (PS) fine-tuning. The evaluation was carried out on 2D cine MRI datasets from two institutions, containing scans from 33 patients with 8060 labeled frames, with annotations from 2 to 5 observers per frame, totaling 29179 ground truth segmentations. The segmentations produced were assessed using the Dice similarity coefficient (DSC), 50% and 95% Hausdorff distances (HD50 / HD95), and the Euclidean center distance (ECD). Main results. The results showed that the contour tracking (median DSC 0.92 +/- 0.04 and ECD 1.9 +/- 1.0 mm) and SAM2-based (median DSC 0.93 +/- 0.03 and ECD 1.6 +/- 1.1 mm) approaches produced target segmentations comparable or superior to TransMorph w/o PS fine-tuning (median DSC 0.91 +/- 0.07 and ECD 2.6 +/- 1.4 mm) and slightly inferior to TransMorph w/ PS fine-tuning (median DSC 0.94 +/- 0.03 and ECD 1.4 +/- 0.8 mm). Between the two novel approaches, the one based on SAM2 performed marginally better at a higher computational cost (inference times 92 ms for contour tracking and 109 ms for SAM2). Both approaches and TransMorph w/ PS fine-tuning exceeded inter-observer variability (median DSC 0.90 +/- 0.06 and ECD 1.7 +/- 0.7 mm). Significance. This study demonstrates the potential of foundation models to achieve high-quality real-time target tracking in MRgRT, offering performance that matches state-of-the-art methods without requiring PS fine-tuning.

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Key words

deep learning,respiratory motion,MRI-linac,MRI-guidance,motion management

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【要点】：本研究评估了两种基于基础人工智能模型的实时靶点跟踪方法，在磁共振成像引导的放射治疗（MRgRT）中的应用效果，其中一种方法使用点跟踪模型，另一种基于SAM2视频对象分割模型，结果显示这两种方法性能接近或优于现有先进方法，且无需患者特异性微调。

【方法】：研究采用了一种点跟踪模型，该模型从参考轮廓传播点，以及一种基于SAM2的视频对象分割模型。

【实验】：实验在两个机构的2D cine MRI数据集上进行，包含33名患者的8060个标记帧，每个帧由2至5名观察者进行注释，共29179个真实标注。评估使用了Dice相似度系数（DSC）、50%和95%豪斯多夫距离（HD50 / HD95）以及欧几里得中心距离（ECD）。结果显示，两种新方法的性能均超过观察者间的变异性。