AI FOR CANCER LESION SEGMENTATION

Abstract

Accurate segmentation of cancer lesions in medical imaging is essential for diagnosis, treatment planning, and monitoring therapeutic response. Manual delineation is labor-intensive, time-consuming, and prone to inter-observer variability. Artificial intelligence (AI), particularly deep learning and convolutional neural networks (CNNs), has emerged as a powerful tool for automated lesion segmentation across modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). This paper reviews current AI-based methodologies for cancer lesion segmentation, highlights their performance in various tumor types, discusses challenges including data scarcity, variability in imaging protocols, and model interpretability, and explores the clinical potential of AI-assisted segmentation to improve precision oncology and patient outcomes.