Abscesses in the head and neck represent an acute infectious process that can potentially lead to sepsis or mortality if not diagnosed and managed promptly. Accurate detection and delineation of these lesions on imaging are essential for diagnosis, treatment planning, and surgical intervention. In this study, we introduce AbscessHeNe, a curated and comprehensively annotated dataset comprising 4,926 contrast-enhanced CT slices with clinically confirmed head and neck abscesses. The dataset is designed to facilitate the development of robust semantic segmentation models that can accurately delineate abscess boundaries and evaluate deep neck space involvement, thereby supporting informed clinical decision-making. To establish performance baselines, we evaluate several state-of-the-art segmentation architectures, including CNN, Transformer, and Mamba-based models. The highest-performing model achieved a Dice Similarity Coefficient of 0.39, Intersection-over-Union of 0.27, and Normalized Surface Distance of 0.67, indicating the challenges of this task and the need for further research. Beyond segmentation, AbscessHeNe is structured for future applications in content-based multimedia indexing and case-based retrieval. Each CT scan is linked with pixel-level annotations and clinical metadata, providing a foundation for building intelligent retrieval systems and supporting knowledge-driven clinical workflows. The dataset will be made publicly available at https://github.com/drthaodao3101/AbscessHeNe.git.

翻译：头颈部脓肿是一种急性感染过程，若未能及时诊断和处理，可能导致脓毒症或死亡。在影像学上准确检测和勾画这些病变对于诊断、治疗规划和手术干预至关重要。本研究介绍了AbscessHeNe，一个经过精心整理且全面标注的数据集，包含4,926张经临床确认的头颈部脓肿增强CT切片。该数据集旨在促进鲁棒语义分割模型的开发，以准确勾画脓肿边界并评估深部颈部间隙受累情况，从而支持基于信息的临床决策。为建立性能基准，我们评估了多种最先进的分割架构，包括基于CNN、Transformer和Mamba的模型。性能最佳的模型实现了0.39的Dice相似系数、0.27的交并比和0.67的归一化表面距离，表明该任务的挑战性及进一步研究的必要性。除分割外，AbscessHeNe的结构设计支持未来在基于内容的多媒体索引和基于案例的检索中的应用。每个CT扫描均关联像素级标注和临床元数据，为构建智能检索系统和支持知识驱动的临床工作流程奠定了基础。该数据集将通过https://github.com/drthaodao3101/AbscessHeNe.git公开提供。