Department of Otorhinolaryngology, Head and Neck Surgery, Justus Liebig University of Giessen, 35392, Giessen, Germany.
Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748, Munich, Germany.
Sci Data. 2023 Oct 21;10(1):733. doi: 10.1038/s41597-023-02629-7.
The endoscopic examination of subepithelial vascular patterns within the vocal fold is crucial for clinicians seeking to distinguish between benign lesions and laryngeal cancer. Among innovative techniques, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) offers real-time visualization of these vascular structures. Despite the advent of CE-NBI, concerns have arisen regarding the subjective interpretation of its images. As a result, several computer-based solutions have been developed to address this issue. This study introduces the CE-NBI data set, the first publicly accessible data set that features enhanced and magnified visualizations of subepithelial blood vessels within the vocal fold. This data set encompasses 11144 images from 210 adult patients with pathological vocal fold conditions, where CE-NBI images are annotated using three distinct label categories. The data set has proven invaluable for numerous clinical assessments geared toward diagnosing laryngeal cancer using Optical Biopsy. Furthermore, given its versatility for various image analysis tasks, we have devised and implemented diverse image classification scenarios using Machine Learning (ML) approaches to address critical clinical challenges in assessing laryngeal lesions.
声带黏膜下血管模式的内镜检查对于试图区分良性病变和喉癌的临床医生至关重要。在创新技术中，接触式内镜检查结合窄带成像（CE-NBI）可实时观察这些血管结构。尽管 CE-NBI 的出现，但其图像的主观解释引起了关注。因此，已经开发了几种基于计算机的解决方案来解决这个问题。本研究介绍了 CE-NBI 数据集，这是第一个公开可用的数据集，其中包含增强和放大的声带黏膜下血管可视化图像。该数据集包含 210 名患有病理声带状况的成年患者的 11144 张图像，其中使用三个不同的标签类别对 CE-NBI 图像进行注释。该数据集对于使用光学活检诊断喉癌的许多临床评估非常有价值。此外，鉴于其在各种图像分析任务中的多功能性，我们使用机器学习 (ML) 方法设计并实施了不同的图像分类场景，以解决评估喉病变的关键临床挑战。
