Enhancing Video Object Segmentation in TrackRAD Using XMem Memory Network
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911171386277888 |
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| author | Deng, Pengchao Chen, Shengqi |
| author_facet | Deng, Pengchao Chen, Shengqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Video Object Segmentation in TrackRAD Using XMem Memory Network Deng, Pengchao Chen, Shengqi Computer Vision and Pattern Recognition 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. |
| title | Enhancing Video Object Segmentation in TrackRAD Using XMem Memory Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.18591 |