Abstract
Accurate tumor tracking in MR-guided radiotherapy is critical for real-time beam gating and adaptive radiotherapy delivery. Prolonged tracking failures risk geometric misses of the target volume. Dynamic MR imaging is usually performed to capture the underlying motion with subsequent segmentation and tracking of the lesion. While foundation models such as SAM2 have shown strong performance in video object segmentation, their memory retrieval mechanisms rely predominantly on appearance-based representations, making them susceptible to object drifts under non-rigid deformable motion. We propose a motion-guided memory modulation framework that augments SAM2 with explicit motion consistency cues without retraining the base model. An optical flow model is used to estimate motion and propagate tumor masks between consecutive frames. Agreement between the propagated mask and the current SAM2 prediction is quantified using an IoU-based consistency metric. The metric acts as a reliability weight to modulate the memory representations prior to retrieval, suppressing unreliable memories before they corrupt the tracking state. Evaluated on the TrackRAD2025 Challenge dataset, our framework markedly improves over both SAM2 and MedSAM2 baselines. Against MedSAM2, we reduce the miss rate from 6.1% to 1.3% (109 additional recovered frames), improve J&F (mean Jaccard and contour F-score) by 3.3 percentage points, and reduce maximum failure duration from 90 to 8 frames, corresponding to approximately 22s versus 2s at clinical cine MRI acquisition rates, while preserving segmentation quality (median Dice 0.884). All variants operate within real-time clinical constraints, processing each frame in under 200ms. These results demonstrate that motion-guided memory modulation is a lightweight, plug-and-play, and clinically motivated strategy for robust tumor tracking in MRI-guided adaptive radiotherapy.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_017.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{MegPau_MotionConsistent_MICCAISAT2026,
author = { Megne Choudja, Pauline Ornela AND Nachbar, Marcel AND Ghoul, Aya AND Gani, Cihan AND Thorwarth, Daniela AND Kuestner, Thomas},
title = { { Motion-Consistent Memory for Foundation Model-Based Tumor Tracking in MR-guided radiotherapy } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}