基于SAM注意力机制的头颈部危及器官 CT图像分割
Automatic Segmentation of Organs-at-Risk in Head-and-Neck Carcinoma from Radiation Therapy Using Multi-Scale Fusion and Attention-Based Mechanisms
摘要: 目的：本文提出了一种基于残差和注意力机制U-Net的头颈部医学影像危及器官分割新方法。方法：在nnU-Net编码阶段，提出将SAM注意力模块与残差方法相结合的残差注意力模块，以增强特征表达能力；在解码阶段引入残差注意力模块，根据分割任务提高特征加权的相关性。结果：该方法基于包含22个头颈部危及器官的真实医学影像数据集进行评估，实验结果表明，与现有方法相比，所提方法的平均分割准确率提升了11.4%。对22种头颈部危及器官分割的平均骰子相似系数(Dice Similarity Coefficient, DSC)为87.2%。结论：基于可分离卷积和注意力机制的U-Net卷积神经网络对鼻咽癌靶区达到了更好的分割精度，表明该方法有望帮助临床医生提高放射治疗的准确性和效率。
Abstract: We proposed a new organs-at-risk segmentation method for medical images of heads and necks based on the U-Net with residuals and attention mechanism. A SAM block combined with a residual method is proposed as Residual-SAM Block in the nnU-Net encoding stage in order to enhance the feature expression ability. SAM are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 11.4% compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 87.2%. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy.
文章引用：林小惟, 张福全. 基于SAM注意力机制的头颈部危及器官 CT图像分割[J]. 临床医学进展, 2026, 16(3): 2644-2650. https://doi.org/10.12677/acm.2026.1631064

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