A Comprehensive High-Resolution Dataset for Analyzing Craniofacial Features in Goldenhar Syndrome: Images for Feature Detection and Medical Diagnosis

This dataset contains 629 high-resolution images annotated with 900 labels across seven classes, specifically curated for craniofacial feature detection and analysis in Goldenhar Syndrome (GA). The dataset includes diverse annotations such as Cleft-Lip-and-Palate, Epibulbar Dermoid Tumor, Eyelid Coloboma, Facial Asymmetry, Malocclusion, Microtia, and Vertebral Abnormalities. Images are uniformly resized to 640x640 pixels and preprocessed with auto-orientation and histogram equalization to enhance contrast for improved feature detection. This dataset is an essential resource for researchers in craniofacial analysis, machine learning, and syndrome-specific diagnostics. It supports advancements in automated feature detection and clinical applications for GA. Its carefully curated structure and rich annotations make it suitable for academic research and real-world applications in automated craniofacial analysis.

本数据集包含629张高分辨率图像，共计标注900个标签，涵盖7个类别，专为Goldenhar综合征（Goldenhar Syndrome, GA）的颅面特征检测与分析任务定制。数据集包含多类标注项，具体为唇腭裂（Cleft Lip and Palate）、眼表皮样瘤（Epibulbar Dermoid Tumor）、眼睑缺损（Eyelid Coloboma）、面部不对称（Facial Asymmetry）、错颌畸形（Malocclusion）、小耳畸形（Microtia）以及椎体异常（Vertebral Abnormalities）。所有图像均统一调整至640×640像素，并通过自动定向与直方图均衡化完成预处理，以增强图像对比度，优化特征检测效果。本数据集是颅面分析、机器学习及综合征特异性诊断领域研究人员的重要资源，可支撑Goldenhar综合征自动化特征检测与临床应用的技术发展。其精心构建的数据集结构与丰富的标注内容，使其适配自动化颅面分析领域的学术研究与实际应用场景。