CeraMIRScan: Mid-infrared OCT Scan Dataset for Ceramic Quality Assessment

Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabilities and extensive applicability across various industrial domains. The CeraMIRScan dataset comprises 29 volumes corresponding to MIR-OCT scans of 3D printed ceramic pieces and has been carefully curated to support the development of Deep Learning models for defect segmentation. Of these, 22 volumes include bounding-box annotations to enable defect localisation and classification, while all volumes are accompanied by manually generated binary segmentation masks. In total, the dataset contains 21,882 individual scans, of which 41.38% exhibit detectable defects. The dataset is organised into three primary components. The first images/ contains the 29 MIR-OCT volumes. The second annotations/raw_labels/ provides bounding-box coordinates and defect-level labels for 22 volumes. The third annotations/masks/ includes pixel-wise segmentation masks for all volumes. Image and mask files follow the naming convention 'VolumeName_SlideNumber.png', and bounding-box annotations are stored as 'VolumeName.csv'.中红外光学相干断层扫描（Mid-infrared Optical Coherence Tomography, MIR-OCT）凭借高分辨率成像能力与跨多工业领域的广泛适用性，是极具应用前景的无损检测（Non-Destructive Testing, NDT）技术。 CeraMIRScan数据集包含29组对应3D打印陶瓷构件MIR-OCT扫描结果的体积数据，该数据集经过精心编录，旨在支持面向缺陷分割任务的深度学习（Deep Learning）模型研发。其中22组体积数据带有边界框标注，可用于缺陷定位与分类任务；所有体积数据均附带人工生成的二值分割掩码。该数据集总计包含21882幅独立扫描图像，其中41.38%的图像存在可检测的缺陷。 该数据集分为三个核心组成部分：第一部分为`images/`目录，存储全部29组MIR-OCT体积扫描数据；第二部分为`annotations/raw_labels/`目录，提供22组体积数据的边界框坐标与缺陷类别标签；第三部分为`annotations/masks/`目录，包含所有体积数据的逐像素分割掩码。图像与掩码文件遵循"VolumeName_SlideNumber.png"的命名规范，边界框标注文件则以"VolumeName.csv"格式存储。