Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings

Oyibo, Prosper, Meulah, Brice, Bengtson, Michel, van Lieshout, Lisette, Oyibo, Wellington, Diehl, Jan-Carel, Vdovine, Gleb and Agbana, Tope 2023. Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings. Journal of Medical Imaging 10 (04) , 044005. 10.1117/1.JMI.10.4.044005

[thumbnail of 044005_1.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (2MB)


Purpose Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis. Approach Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator. Result The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference. Conclusion Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
ISSN: 2329-4302
Date of First Compliant Deposit: 20 November 2023
Date of Acceptance: 21 July 2023
Last Modified: 20 Nov 2023 10:15

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics