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Related Experiment Video

Updated: Jan 30, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
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M $^{3}$ SegNet: A Multi-Modal and Multi-Branch Framework for Nasopharyngeal Carcinoma Segmentation in Radiotherapy

Junqiang Ma, Luyi Han, Henry H Y Tong

    IEEE Journal of Biomedical and Health Informatics
    |January 28, 2026
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    Summary

    This study introduces M³SegNet, an automated system for precise nasopharyngeal carcinoma radiotherapy planning. It accurately labels tumor volumes and organs at risk using multi-modal imaging, improving upon manual methods.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Radiotherapy Planning

    Background:

    • Accurate segmentation of gross tumor volumes, clinical target volumes, and organs at risk is crucial for nasopharyngeal carcinoma (NPC) radiotherapy.
    • Manual segmentation is time-consuming and prone to inter-observer variability, hindering multi-modal CT and MRI interpretation.
    • Automated methods, particularly multi-modal and multi-task learning, show promise but require improved feature fusion, anatomical prior integration, and unified frameworks.

    Purpose of the Study:

    • To develop a novel automated framework, M³SegNet, for simultaneous multi-modal segmentation in NPC radiotherapy planning.
    • To address limitations in clinical adoption by enhancing multi-modal feature fusion and incorporating anatomical priors.
    • To provide a reliable and clinically translatable solution for automated segmentation tasks.

    Main Methods:

    • Proposed M³SegNet, a multi-modal, multi-branch framework for concurrent segmentation of all relevant structures.
    • Introduced Synergistic Global-Local Attention for fusing features from CT, T1-weighted, T2-weighted, and T1 contrast MRI.
    • Implemented Anatomy-Aware Hierarchical Learning using OAR spatial information to guide tumor segmentation and Random Modality Dropout for robustness.

    Main Results:

    • M³SegNet demonstrated significant outperformance compared to state-of-the-art methods on internal and external datasets.
    • The framework effectively leverages multi-modal information and anatomical priors for accurate segmentation.
    • Validation on a 257-patient dataset confirmed the generalizability and reliability of the proposed approach.

    Conclusions:

    • M³SegNet offers a reliable, automated solution for NPC radiotherapy planning by integrating multi-modal imaging and anatomical guidance.
    • The proposed attention mechanism and learning strategy enhance segmentation accuracy and robustness.
    • This framework represents a clinically translatable advancement for improving radiotherapy planning efficiency and consistency.