BEATing CILIAted cells

Nasal cytology is a medicine field that focuses on the examination of nasal mucosa cells with the objective of recognizing changes in the epithelium, which is frequently subjected to acute or chronic irritation and inflammation caused by viruses, bacteria, or fungi; in the last decade nasal cytology is becoming increasingly critical in diagnosing nasal conditions. Among the cell population of the nasal mucosa, ciliated cells make up 80% of the epithelium in the upper airways. These cells are characterized by the presence of cilia that normally beat in a consistent, fluid pattern, easing effective mucociliary clearance. This cytotype results quite important since its study allows for the diagnosis of primary ciliary dyskinesia (PCD), a rare pathology that is usually associated with other severe pathologies such as situs inversus, cardiac diseases and male infertility. The assessment of ciliary function through the measurement of the ciliary beating frequency (CBF).In addition, the CBF decay of the ciliated cells in a dead body may help to trace the time of death. These motivations have fueled the research seeking an architecture that supports such long, complex estimations, and feels necessary in medical andforensics contexts, that’s why we developed Deep Cilia, a completely autonomous CBFdecay estimation system. We also collected a new dataset containing videos of beating ciliated cells, made available to the Scientific Community on IEEE Dataport. The videos were acquired by an expert cytologist using a microscope (with 1000x magnification) equipped with a digital camera. The dataset contains 119 videos of beating ciliated cells and each video exhibits a variable number of ciliated cells, varying from 1 to more than 5 (in contexts where many cells are amassed). The average video duration in the dataset is 93 seconds, with an upper bound of 352 seconds and the shortest being only 3 seconds long. As underlined, the cardinality of ciliated cells recorded in each video can vary. From the 119 videos a total of 246 clearly visible strias were reported, labeled and manually extracted by the authors on each frame of the videos. In addition to the number of cells and video duration, the videos differ from each other in terms of frame rate with an average of 23.63 Fps and an standard deviation of ± 2.54 Fps and in terms of resolution. The video frames are also already extracted, augmented and splitted in three partitions to train, validate and test eventual object detection systems, alongside the bounding boxes for the cells manually extracted by the authors on each frame.鼻细胞学（Nasal cytology）是一门专注于鼻黏膜细胞检测的医学领域，旨在识别上皮细胞的变化，而上皮细胞常因病毒、细菌或真菌受到急性或慢性刺激并引发炎症。近十年来，鼻细胞学在鼻部疾病诊断中的重要性日益凸显。在鼻黏膜的细胞群中，纤毛细胞（ciliated cells）占上呼吸道上皮细胞的80%。这类细胞以纤毛的存在为特征，正常情况下纤毛会以稳定的节律摆动，促进有效的黏液纤毛清除功能。这种细胞类型的研究具有重要意义，因为其可用于诊断原发性纤毛运动障碍（primary ciliary dyskinesia, PCD）——一种罕见的病理状态，通常与内脏转位、心脏疾病及男性不育等其他严重病症相关。可通过测量纤毛摆动频率（ciliary beating frequency, CBF）评估纤毛功能。此外，死亡个体体内纤毛细胞的CBF衰减情况，有助于推断死亡时间。上述研究动机推动了对支持此类长时复杂估算的架构的探索，而这类架构在医学及法医场景中必不可少。为此，我们开发了Deep Cilia，一款完全自主的CBF衰减估算系统。我们还收集了全新的数据集，内含纤毛细胞摆动的视频，并已在IEEE Dataport向科研社群开放。这些视频由专业细胞学家使用配备数码相机的显微镜（放大倍率1000倍）采集。数据集包含119段纤毛细胞摆动视频，每段视频中的纤毛细胞数量不一，从1个到5个以上（在细胞大量聚集的场景中）。数据集中视频的平均时长为93秒，最长可达352秒，最短仅3秒。如前所述，每段视频中记录的纤毛细胞数量存在差异。在119段视频中，作者共标注并手动提取了246个清晰可见的纤毛纹，并在每帧视频中完成了标注。除细胞数量与视频时长外，各视频的帧率与分辨率也存在差异：平均帧率为23.63帧/秒，标准差为±2.54帧/秒。视频帧已完成提取、增强，并划分为训练集、验证集与测试集三个分区，以供后续目标检测系统训练使用，同时附带作者在每帧中手动标注的细胞边界框。