Shah Mohammad Imran, Mishra Smriti, Yadav Vinod Kumar, Chauhan Arun, Sarkar Malay, Sharma Sudarshan K, Rout Chittaranjan
Jaypee University of Information Technology, Department of Biotechnology and Bioinformatics, Waknaghat, Himachal Pradesh, India.
Indira Gandhi Medical College, Department of Pulmonary Medicine, Shimla, India.
J Med Imaging (Bellingham). 2017 Apr;4(2):027503. doi: 10.1117/1.JMI.4.2.027503. Epub 2017 Jun 30.
Ziehl-Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl-Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.
萋-尼染色显微镜检查是检测结核病的一项关键细菌学检测方法，但其灵敏度较低。根据世界卫生组织（WHO）的建议，应分析300个视野以提高灵敏度，但由于患者数量众多，实际仅检查了少数视野。因此，通过自动捕获聚焦图像（自动对焦）、拼接视野以形成全景图（自动拼接）以及自动分割杆菌（分级）来诊断结核病，可显著提高灵敏度。然而，缺乏统一的数据集阻碍了这三个领域中强大算法的开发。因此，已经开发了萋-尼痰涂片显微镜图像数据库（ZNSM iDB），并且可以免费获取。该数据库包含从三种不同明场显微镜获取的七类不同数据集。与自动对焦、自动拼接以及手动分割杆菌相关的数据集可用于开发算法，而其他四类数据集则用于优化灵敏度和特异性。所有三类数据集均使用不同的自动化算法进行了验证。由于该数据库中的图像具有独特的呈现方式，且噪声和伪影较高，因此该参考资源也可用于验证强大的检测算法。ZNSM-iDB还有助于自动显微镜方法的开发。
