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.

通过使用 2D 戊二醛 - 席夫碱（PAS）染色组织学图像，本研究在基础的 FastAI U-Net 模型基础上，探讨了替代骨干架构、深层模型以及特征金字塔网络。通过与基线 U-Net 模型进行基准测试，我们对这些不同的方法进行了严格评估，从而提供了有关前沿分割技术的全面探索，为未来研究提供了有价值的见解。