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Microvasculature Segmentation in Human BioMolecular Atlas Program (HuBMAP)

arXiv 2308.03203 ↗First posted 6 Aug 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 6 Aug 2023.

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22 of 30 references.

References · 22

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10 more
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