Advances in radiation oncology · 2026
Multimodality Multimask and Multitask Auto-Segmentation Network for Organs-at-Risk in Head and Neck Radiation Therapy.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ni Xiaochen, Tang Tianci, Li Shengwei, Jia Lecheng, Wei Ziquan, Zhu Yi, Li Ji, Wang Weifang, Song Xinmao, Zou Lifen, Zhang Haiyan, Tian Shu, Yan Li, Yang Gang, Chen Fu, Wang Tian, Wang Li, Wang Jie, Li Ruichen, Ma Siyuan, Wang Xiaoshen
Аннотация
PURPOSE: Accurate segmentation of head and neck organs-at-risk remains a critical challenge in radiation therapy planning, where current single-modality approaches often fail to address the inherent complexity of soft-tissue differentiation and interpatient anatomic variations. This study aims to develop a clinically robust auto-segmentation framework that synergistically integrates multimodal imaging features while optimizing computational efficiency.
METHODS AND MATERIALS: We present multimodality multimask and multitask auto-segmentation network (M3-Net), a triple-interlocked deep learning architecture featuring: (1) cross-modality fusion modules with attention-guided feature recalibration between computed tomography density maps and magnetic resonance imaging soft-tissue contrast; (2) a hierarchical multimask generator producing organ-specific, regional, and global masks through parallel encoding pathways; and (3) a dual-task learning mechanism combining segmentation with deformable image registration to establish voxel-level modality correspondence. The model was trained on 200 retrospective cases (160/20/20 split) with expert-reviewed contours from a tertiary cancer center, supplemented by 10 prospective cases for clinical validation.
RESULTS: M3-Net demonstrated significant improvements across 3 key dimensions: Efficiency: reduced inference time by 63.6% (548 ± 23 seconds vs 198 ± 15 seconds; P < .001) through dynamic mask prioritization. These strategies improved the performance of M3-Net. Sixty percent of the organs achieved a Dice similarity coefficient >0.88. M3-Net performed best in 93.3% of all organs. It achieved the best average surface distance for all organs. For independent test cases, the speed and precision can meet clinical requirements.
CONCLUSIONS: M3-Net establishes new state-of-the-art performance for head and neck organs-at-risk segmentation, by simultaneously addressing accuracy-efficiency tradeoffs and modality discordance. The clinically validated workflow reduces contouring time by 75% while maintaining dosimetrically significant precision, enabling rapid adoption in adaptive radiation therapy protocols.
METHODS AND MATERIALS: We present multimodality multimask and multitask auto-segmentation network (M3-Net), a triple-interlocked deep learning architecture featuring: (1) cross-modality fusion modules with attention-guided feature recalibration between computed tomography density maps and magnetic resonance imaging soft-tissue contrast; (2) a hierarchical multimask generator producing organ-specific, regional, and global masks through parallel encoding pathways; and (3) a dual-task learning mechanism combining segmentation with deformable image registration to establish voxel-level modality correspondence. The model was trained on 200 retrospective cases (160/20/20 split) with expert-reviewed contours from a tertiary cancer center, supplemented by 10 prospective cases for clinical validation.
RESULTS: M3-Net demonstrated significant improvements across 3 key dimensions: Efficiency: reduced inference time by 63.6% (548 ± 23 seconds vs 198 ± 15 seconds; P < .001) through dynamic mask prioritization. These strategies improved the performance of M3-Net. Sixty percent of the organs achieved a Dice similarity coefficient >0.88. M3-Net performed best in 93.3% of all organs. It achieved the best average surface distance for all organs. For independent test cases, the speed and precision can meet clinical requirements.
CONCLUSIONS: M3-Net establishes new state-of-the-art performance for head and neck organs-at-risk segmentation, by simultaneously addressing accuracy-efficiency tradeoffs and modality discordance. The clinically validated workflow reduces contouring time by 75% while maintaining dosimetrically significant precision, enabling rapid adoption in adaptive radiation therapy protocols.