  Image segmentation serves as a critical tool across a range of applications,
encompassing autonomous driving's pedestrian detection and pre-operative tumor
delineation in the medical sector. Among these applications, we focus on the
National Institutes of Health's (NIH) Human BioMolecular Atlas Program
(HuBMAP), a significant initiative aimed at creating detailed cellular maps of
the human body. In this study, we concentrate on segmenting various
microvascular structures in human kidneys, utilizing 2D Periodic Acid-Schiff
(PAS)-stained histology images. Our methodology begins with a foundational
FastAI U-Net model, upon which we investigate alternative backbone
architectures, delve into deeper models, and experiment with Feature Pyramid
Networks. We rigorously evaluate these varied approaches by benchmarking their
performance against our baseline U-Net model. This study thus offers a
comprehensive exploration of cutting-edge segmentation techniques, providing
valuable insights for future research in the field.
