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

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

Oyibo, P.O. (TU Delft Team Michel Verhaegen; University of Lagos)
Meulah, Brice (Leiden University Medical Center; Centre de Recherches Medicales des Lambaréné, Lambarene)
Bengtson, Michel (Leiden University Medical Center)
Lieshout, Lisette van (Leiden University Medical Center)
Oyibo, Wellington (University of Lagos)
Diehl, J.C. (TU Delft Design for Sustainability)
Vdovin, Gleb (TU Delft Team Michel Verhaegen)
Agbana, T.E. (TU Delft Team Michel Verhaegen)

2023

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.

schistosomiasis diagnosis
deep learning
semantic segmentation
ellipse fitting
edge artificial intelligence

http://resolver.tudelft.nl/uuid:b8ce037d-72e7-468a-927e-06bef2f3f081

https://doi.org/10.1117/1.JMI.10.4.044005

2329-4302

Journal of Medical Imaging, 10 (4)

Institutional Repository

journal article

© 2023 P.O. Oyibo, Brice Meulah, Michel Bengtson, Lisette van Lieshout, Wellington Oyibo, J.C. Diehl, Gleb Vdovin, T.E. Agbana