Automated Nuclei Detection in Microscopy Images

Abstract

Automated nuclei detection and segmentation in microscopy images represents a critical advancement in biomedical research and drug discovery pipelines. This study presents the development and evaluation of a deep learning approach utilizing the U-Net convolutional neural network architecture for nuclei segmentation using the 2018 Data Science Bowl dataset. U-Net achieved an IoU score of 0.88 excelling in complex cellular structures and addressing challenges like overlapping nuclei and diverse image quality. Image segmentation constitutes a foundational step in biomedical image analysis that impacts all subsequent analytical processes. While manual segmentation by pathologists remains time-consuming and subjective, our automated approach offers consistent, accurate, and efficient nuclei delineation. In the context of Nepal, where medical and research infrastructure faces resource constraints, U-Net-based segmentation tools hold promise for enhancing diagnostic capabilities in medical institutions, enabling efficient analysis of locally relevant disease samples, and creating opportunities for Nepali researchers to contribute to global biomedical research despite limited resources. This research demonstrates that U-Net architecture effectively balances computational efficiency with segmentation accuracy for automated nuclei detection, with potential applications for strengthening medical image analysis capabilities in emerging research environments.