StarCell: A Web Platform for Efficient Cell Segmentation and Analysis

Cell segmentation is a fundamental task in biomedical image analysis, particularly in pathology, cancer research, and diagnostics. Deep learning-based approaches, especially convolutional neural networks (CNNs), have significantly improved the accuracy of cell segmentation. In this study, we present a web-based platform that integrates StarDist, a state-of-the-art deep learning model specifically designed for nucleus segmentation. The system provides an intuitive interface for researchers and clinicians to upload images, apply pre-trained and custom-trained StarDist models, visualize segmentation results, and download statistical data for further analysis. This framework enables real-time nucleus segmentation without requiring specialized computational resources, making advanced deep learning-based segmentation more accessible to non-expert users. Finally, we evaluate the performance of different StarDist models on the SIPaKMeD dataset, assessing metrics such as precision, recall, accuracy, Intersection over Union (IoU), to validate their effectiveness and to provide insights into the trade-off between model complexity and segmentation accuracy.