SAM in the Pipeline: Transforming Axis-Aligned to Oriented Bounding Boxes for Superior Sperm Detection

Sperm analysis, in terms of finding the best cells, has traditionally been performed manually and is slow, expensive, prone to human error, and often inconsistent across different laboratory technicians. Our research focuses on the use of artificial intelligence to improve computer-assisted sperm analysis, a critical aspect in the diagnosis and treatment of male fertility issues. Using the best publicly available dataset for sperm detection, we employ novel data refining techniques and state-of-the-art object detection models to develop the best-performing sperm detection model to our knowledge. We show how the Segment Anything Model (SAM) can be used as a data-refining tool to improve object detection datasets. Additionally, we use SAM to convert Axis-Aligned Bounding Box (AABB) to Oriented Bounding Box (OBB) to get more descriptive coordinates. We test our data-refining pipeline with state-of-the-art object detection models and a publicly available sperm-tracking dataset. Our results show that the refined data coming from our pipeline can be used to train lighter models while still achieving higher accuracy compared to using the original dataset. This also enables possibilities for running high-quality tracking on light hardware, such as consumer-grade laptops.