Deep Learning Approaches for Automated Organ Segmentation in Large-Scale CT Cohorts

Abstract

Automated organ segmentation in computed tomography (CT) imaging plays a pivotal role in clinical diagnosis, treatment planning, and large-scale population studies. Traditional segmentation techniques are often limited by variability in organ shape, size, and contrast, making them less effective in handling large and diverse datasets. Recent advancements in deep learning have introduced robust approaches that significantly improve segmentation accuracy and efficiency. This article reviews state-of-the-art deep learning models, including convolutional neural networks (CNNs), U-Net architectures, and transformer-based frameworks, for organ segmentation in large-scale CT cohorts. We discuss training strategies, data augmentation, and transfer learning methods that enhance model generalization across heterogeneous datasets. Furthermore, the study explores challenges such as class imbalance, annotation scarcity, and computational costs, alongside potential solutions like weakly supervised learning and federated learning. The findings demonstrate that deep learning not only accelerates automated organ segmentation but also contributes to scalable and reproducible pipelines for clinical and research applications.