用XMem记忆网络提升MRI实时肿瘤分割精度
Enhancing Video Object Segmentation in TrackRAD Using XMem Memory Network
- 基于带记忆机制的XMem模型追踪长序列MRI中的肿瘤
- 在标注数据少的情况下仍保持较高分割准确率
- 适合需要实时精准肿瘤追踪的放疗临床场景
本文提出一种用于实时MRI引导放疗的肿瘤分割框架，专为TrackRAD2025挑战设计。该方法采用内存增强型XMem模型，对长时序动态MRI序列中的肿瘤进行分割。所提系统通过高效集成记忆机制，在有限标注数据条件下实现对肿瘤运动的实时追踪，保持高分割精度。尽管详细实验记录已丢失，无法提供精确量化结果，但开发阶段的初步评估显示，XMem框架展现出合理分割性能，并满足临床实时性要求。本工作有助于提升放疗中肿瘤追踪的精度，对提高癌症治疗的准确性与安全性具有重要意义。
原文摘要 · Abstract (English)
This paper presents an advanced tumor segmentation framework for real-time MRI-guided radiotherapy, designed for the TrackRAD2025 challenge. Our method leverages the XMem model, a memory-augmented architecture, to segment tumors across long cine-MRI sequences. The proposed system efficiently integrates memory mechanisms to track tumor motion in real-time, achieving high segmentation accuracy even under challenging conditions with limited annotated data. Unfortunately, the detailed experimental records have been lost, preventing us from reporting precise quantitative results at this stage. Nevertheless, From our preliminary impressions during development, the XMem-based framework demonstrated reasonable segmentation performance and satisfied the clinical real-time requirement. Our work contributes to improving the precision of tumor tracking during MRI-guided radiotherapy, which is crucial for enhancing the accuracy and safety of cancer treatments.
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