Instance Segmentation as a Foundation for Quantitative Image Analysis in Leptospira Research

The microscopic agglutination test (MAT) is the gold standard for leptospirosis serodiagnosis, but its reliance on subjective visual assessment leads to interobserver variability. Artificial intelligence (AI) offers a powerful pathway to overcome this limitation, enabling objective and reproducible quantification. The core feature of this technique lies in its ability to pinpoint the precise location and morphology of individual bacteria through instance segmentation. By identifying each bacterium at the pixel level, researchers can not only calculate agglutination rates in MAT based on objective physical definitions but also unlock diverse applications such as morphological profiling and motility analysis when integrated with object tracking techniques. This allows for more granular assessments of drug effects and fundamental bacterial behavior. This chapter provides a comprehensive, end-to-end guide for implementing advanced AI-driven analysis of Leptospira images (including MAT samples), covering the entire workflow from foundational data preparation to the deployment of sophisticated deep learning models.