DOI: 10.5281/zenodo.6467268
title: Schistosoma Haematobium Egg Image Dataset

description: 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 <em>Schistosoma haematobium</em> 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 <em>Schistosoma haematobium</em> 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." <em>Micromachines</em>, 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." <em>Journal of Medical Imaging</em>. [Accepted Manuscript]
description: This research was funded by NWO-WOTRO Science for Global Development program, Grant Number W 07.30318.009 (INSPiRED—INclusive diagnoStics for Poverty REIated parasitic Diseases in Nigeria and Gabon).