Automated Cell Segmentation in Microscopic Images Using Deep Learning Techniques for Biomedical Application

Abstract

Automating the laborious task of cell detection, segmentation, classification, and counting in microscopic images presents a transformative opportunity in biomedical research. Manual and semi-automated methods commonly used by biologists are time consuming, prone to subjective bias, and difficult to scale for large experimental datasets. In this study, we propose an automated method based on deep convolutional neural networks (DCNN) that accurately analyzes complex microscopy images. The approach significantly improves performance over traditional image processing techniques by effectively identifying diverse and irregular cell morphologies. It also enables precise cell classification and counting, helping to quantify surface markers, transcription factors, and cytokine profiles more efficiently. These tasks typically require extensive manual annotation, large cell populations, and multiple biomarkers. The proposed system incorporates visual reasoning capabilities to automate the masking and enumeration of specific cell types, thereby accelerating biological discovery and minimizing human interpretation. This advancement facilitates a scalable, reproducible, and intelligent pipeline for image based cellular analysis.