Head and neck masses are space-occupying lesions that can compress the airway and esophagus and may affect nerves and blood vessels. Available public datasets primarily focus on malignant lesions and often overlook other space-occupying conditions in this region. To address this gap, we introduce MasHeNe, an initial dataset of 3,779 contrast-enhanced CT slices that includes both tumors and cysts with pixel-level annotations. We also establish a benchmark using standard segmentation baselines and report common metrics to enable fair comparison. In addition, we propose the Windowing-Enhanced Mamba with Frequency integration (WEMF) model. WEMF applies tri-window enhancement to enrich the input appearance before feature extraction. It further uses multi-frequency attention to fuse information across skip connections within a U-shaped Mamba backbone. On MasHeNe, WEMF attains the best performance among evaluated methods, with a Dice of 70.45%, IoU of 66.89%, NSD of 72.33%, and HD95 of 5.12 mm. This model indicates stable and strong results on this challenging task. MasHeNe provides a benchmark for head-and-neck mass segmentation beyond malignancy-only datasets. The observed error patterns also suggest that this task remains challenging and requires further research. Our dataset and code are available at https://github.com/drthaodao3101/MasHeNe.git.

翻译：头颈部肿块是可能压迫气道与食道、并影响神经血管的占位性病变。现有公开数据集主要聚焦于恶性病变，常忽视该区域其他占位性病变。为填补这一空白，我们提出MasHeNe——一个包含3,779张增强CT切片、涵盖肿瘤与囊肿并带有像素级标注的初始数据集。我们同时建立了基于标准分割基线的基准，并报告了通用指标以实现公平比较。此外，我们提出了窗口增强型Mamba频域融合（WEMF）模型。WEMF在特征提取前采用三窗口增强以丰富输入表现，并利用多频注意力在U形Mamba骨干网络中融合跳跃连接信息。在MasHeNe数据集上，WEMF在所有评估方法中取得最佳性能：Dice系数70.45%、交并比66.89%、归一化表面距离72.33%、95%豪斯多夫距离5.12毫米。该模型表明在此挑战性任务中具有稳定而强劲的表现。MasHeNe为超越仅含恶性肿瘤数据集的头颈部肿块分割提供了基准。观察到的误差模式也表明该任务仍具挑战性，需进一步研究。我们的数据集与代码已公开于https://github.com/drthaodao3101/MasHeNe.git。