# Image-based quantification of Plasmodium sporozoites in a single oocyst using deep learning-based segmentation and 3D reconstruction

> **NIH NIH R03** · JOHNS HOPKINS UNIVERSITY · 2024 · $81,875

## Abstract

Project Summary
Malaria remains an important public health issue. The oocyst stage is the most under-studied
stage of the malaria parasite due to technical difficulties in isolating and staining these parasites
and the lack of robust in vitro cultures. This is the expansion phase of the parasite in the mosquito
host yet we have only a rudimentary understanding of the magnitude of this expansion: To date,
only two studies have attempted to quantify the number of sporozoites in single oocysts, likely
due to the time-consuming nature of manual counting. To overcome this and perform more
robust quantification, we will develop methods to accurately quantify sporozoites in single
oocysts using super-resolution imaging and deep-learning based image processing pipelines.
The methodology will be validated by manual quantification. Furthermore, we will use the method
to investigate mosquito species-specific differences in the magnitude of oocyst maturation using
different parasite isolates in Asian and African mosquito vectors. This method can be further
developed to classify oocyst microstructures and enable in-depth studies of stage specific gene
and protein expression over the entire developmental cycle of the oocyst in combination with in
situ -omics methodologies. It is also anticipated that an accurate assessment of the magnitude
of oocyst maturation will improve our understanding of the quantitative dynamics of parasite
transmission from mosquito to mammalian host.

## Key facts

- **NIH application ID:** 10791435
- **Project number:** 1R03AI180804-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Sachie Kanatani
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $81,875
- **Award type:** 1
- **Project period:** 2023-12-01 → 2025-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10791435

## Citation

> US National Institutes of Health, RePORTER application 10791435, Image-based quantification of Plasmodium sporozoites in a single oocyst using deep learning-based segmentation and 3D reconstruction (1R03AI180804-01). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10791435. Licensed CC0.

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