Schistosoma Haematobium Egg Image Dataset

This dataset comprises the following components: 1. SHdataset: It contains 12,051 microscopic images taken from 103 urine samples, along with their corresponding segmentation masks manually annotated for Schistosoma haematobium eggs. The dataset is randomly partitioned into 80-20 train-test splits. 2. diagnosis_test_dataset: This dataset includes 65 clinical urine samples. Each sample consists of 117 Field-of-View (FoV) images required to capture the entire filter membrane. Additionally, the dataset includes the diagnosis results provided by an expert microscopist. Samples were obtained from school-age children who had observed the presence of blood in their urine. These clinical urine samples were collected in 20 mL sterile universal containers as part of a field study conducted in the Federal Capital Territory (FCT), Abuja, Nigeria, in collaboration with the University of Lagos, Nigeria. The study received ethical approval from the Federal Capital Territory Health Research Ethics Committee (FCT-HREC) Nigeria (Reference No. FHREC/2019/01/73/18-07-19). The standard urine filtration procedure was used to process the clinical urine samples. Specifically, 10 mL of urine was passed through a 13 mm diameter filter membrane with a pore size of 0.2 μm. After filtration, the membrane was placed on a microscopy glass slide and covered with a coverslip to enhance the flatness of the membrane for image capture. The images were acquired using a digital microscope called the Schistoscope and were saved in PNG format with a resolution of 2028 X 1520 pixels and a size of approximately 2 MB. The annotation and microscopy analysis were performed by a team of two experts from the ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, and Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene. The experts used the coco annotation tool to annotate the 12,051 images, creating polygons around the Schistosoma haematobium eggs. The output of the annotation process was a JSON file containing specific details about the image storage location, size, filename, and coordinates of all annotated regions. The segmentation mask images were generated from the JSON file using a Python program. The SHdataset was used to develop an automated diagnosis framework for urogenital schistosomiasis, while the diagnosis_test_dataset was used to compare the performance of the developed framework with the results from the expert microscopist. For further details about the dataset, more information can be found in the following articles: 1. Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., Bengtson, M., van Lieshout, L., Oyibo, W., Vdovine, G., and Diehl, J.C. (2022). "Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings." Micromachines, 13(5), p.643. 2. Oyibo, P., Meulah, B., Bengtson, M., van Lieshout, L., Oyibo, W., Diehl, J.C., Vdovine, G., and Agbana, T. (2023). "Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings." Journal of Medical Imaging. [Accepted Manuscript]

本数据集包含以下组成部分：1. 血吸虫卵数据集（SHdataset）：包含源自103份尿液样本的12051张显微图像，以及对应手工标注的埃及血吸虫（Schistosoma haematobium）虫卵分割掩码。该数据集按80-20的比例随机划分为训练集与测试集。2. 诊断测试数据集（diagnosis_test_dataset）：包含65份临床尿液样本，每份样本需采集117张视野（Field-of-View, FoV）图像以覆盖整个滤膜。该数据集同时包含专家显微镜技师出具的诊断结果。样本取自观察到血尿的学龄儿童，作为在尼日利亚阿布贾联邦首都特区（Federal Capital Territory, FCT）开展的实地研究的一部分，由尼日利亚拉各斯大学合作完成。本研究已通过尼日利亚联邦首都特区健康研究伦理委员会（Federal Capital Territory Health Research Ethics Committee, FCT-HREC）的伦理审批（审批编号：FHREC/2019/01/73/18-07-19）。临床尿液样本采用标准尿液过滤流程处理：取10 mL尿液通过直径13 mm、孔径0.2 μm的滤膜。过滤完成后，将滤膜置于显微镜载玻片上并加盖玻片，以平整滤膜便于图像采集。使用名为Schistoscope的数字显微镜采集图像，图像以PNG格式存储，分辨率为2028×1520像素，单张大小约2 MB。标注与显微镜分析由来自尼日利亚拉各斯大学医学院疟疾诊断卓越中心（ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos）及兰巴雷内医学研究中心（Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene）的两名专家团队完成。专家使用COCO标注工具对12051张图像进行标注，通过多边形框标注埃及血吸虫虫卵。标注流程输出为JSON文件，包含图像存储路径、尺寸、文件名及所有标注区域的坐标等详细信息。分割掩码图像通过Python程序从该JSON文件生成。本血吸虫卵数据集用于开发泌尿生殖道血吸虫病自动化诊断框架，而诊断测试数据集则用于对比该开发框架与专家显微镜技师的诊断性能。如需了解该数据集的更多细节，可参阅以下文献：1. Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., Bengtson, M., van Lieshout, L., Oyibo, W., Vdovine, G., and Diehl, J.C. (2022). "Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings." Micromachines, 13(5), p.643. 2. Oyibo, P., Meulah, B., Bengtson, M., van Lieshout, L., Oyibo, W., Diehl, J.C., Vdovine, G., and Agbana, T. (2023). "Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings." Journal of Medical Imaging. [Accepted Manuscript]