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March 18, 2026Physics in Medicine and BiologyOpen Access

Text-guided automatic segmentation of clinical target volume in rectal cancer radiotherapy

Hao Peng Yuting Liang S N Wei Qian Liu Xinyuan Chen Yuan Tang Kuo Men X. Dai

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Key Points

The research aims to improve the segmentation of clinical target volumes in rectal cancer treatment using text-guided methods.
Integrated clinical semantics with imaging techniques
Compared TG-SegNet to nnU-Net and ablated models
Evaluated accuracy, efficiency, and clinical acceptability
TG-SegNet showed superior accuracy compared to nnU-Net
Demonstrated increased efficiency in clinical applications
Achieved high clinical acceptability for usage in radiotherapy

Implication

Text-guided segmentation enhances accuracy and efficiency in rectal cancer radiotherapy, suggesting clinical potential.

Abstract

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Cite This Study

Peng et al. (2026) studied this question.

synapsesocial.com/papers/69be362d6e48c4981c674e54 https://doi.org/10.1088/1361-6560/ae5440

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1MGTP-Seg: A Mask Guidance and Text Prompting Network for Gross Tumor Volume Segmentation in Esophageal Cancer RadiotherapyOriginal Research · 2026
2CT-based auto-segmentation of multiple target volumes for all-in-one radiotherapy in rectal cancer patients2025 · 2 citations
3Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework2024 · 14 citations
4Enhancing GI Cancer Radiation Therapy: Advanced Organ Segmentation with ResECA-U-Net Model2024 · 21 citations
5Improved deep learning for automatic localisation and segmentation of rectal cancer on T2‐weighted MRI2024 · 5 citations

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