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

Prosper Oyibo; Brice Meulah; Michel Bengtson; Lisette van Lieshout; Wellington Oyibo; Jan-Carel Diehl; Gleb Vdovine; Tope Agbana

doi:10.1117/1.JMI.10.4.044005

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

J Med Imaging (Bellingham). 2023 Jul;10(4):044005. doi: 10.1117/1.JMI.10.4.044005. Epub 2023 Aug 7.

Authors

Prosper Oyibo^{1

2}, Brice Meulah^{3

4}, Michel Bengtson³, Lisette van Lieshout³, Wellington Oyibo², Jan-Carel Diehl⁵, Gleb Vdovine¹, Tope Agbana¹

Affiliations

¹ Delft University of Technology, Delft Center for Systems and Control, Faculty of Mechanical, Maritime, and Materials Engineering, Delft, The Netherlands.
² University of Lagos, College of Medicine, Centre for Malaria Diagnosis, NTD Research, Training, and Policy/ANDI Centre of Excellence for Malaria Diagnosis, Lagos, Nigeria.
³ Leiden University Medical Centre, Department of Parasitology, Leiden, The Netherlands.
⁴ Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene, Gabon.
⁵ Delft University of Technology, Department of Sustainable Design Engineering, Faculty of Industrial Design Engineering, Delft, The Netherlands.

PMID: 37554627
PMCID: PMC10405291 (available on 2024-08-07)
DOI: 10.1117/1.JMI.10.4.044005

Abstract

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.

Keywords: deep learning; edge artificial intelligence; ellipse fitting; schistosomiasis diagnosis; semantic segmentation.