Decoding Cellular Structures: A Collaborative Exploration of Nuclei Segmentation with U-NET & YoLoV8L

Nuclei segmentation is a critical component of medical image analysis, holding paramount importance in decoding cellular structures for disease diagnosis and treatment planning. Accurate segmentation facilitates precise quantification of cellular features, empowering clinicians and researchers to unravel disease complexities and advance personalized medicine. This collaborative research uses nuclei segmentation using deep learning techniques, evaluating models such as UNet and YOLOv8L on the diverse MoNuSeg dataset. The research work methodology, detailed with GPU specifications, dataset nuances, and model architectures, contributes valuable insights into cellular dynamics. Results showcase segmentation accuracy, with YOLOv8L’s larger patches enhancing performance. This collaborative effort serves as a significant contribution to medical image analysis, paving the way for innovations that shape the landscape of nuclei segmentation.