A REVIEW ON DEEP LEARNING- BASED SEGMENTATION ALGORITHMS FOR MEDICAL IMAGES

Abstract

Medical image segmentation is a crucial task in healthcare, enabling accurate diagnosis, 
treatment planning, and disease monitoring. Traditional segmentation techniques, such as thresholding, 
region-growing, and deformable models, often struggle with handling complex anatomical structures 
and noise. Deep learning-based approaches, particularly convolutional neural networks (CNNs), 
recurrent neural networks (RNNs), and hybrid models, have emerged as powerful alternatives, offering 
improved accuracy and automation. This paper provides a comprehensive review of deep learning- 
based segmentation algorithms, discussing their methodologies, advantages, and limitations. 
Additionally, we explore the applications of these models across various medical imaging modalities, 
such as MRI, CT, and ultrasound. The challenges of deep learning models, including data dependency, 
computational requirements, and generalization, are also addressed. Finally, we highlight future 
research directions, emphasizing the need for self-supervised learning, federated learning, and 
explainability in deep learning-driven medical image segmentation.