An Overview of Supervised Deep Learning Methods for Medical Image Segmentation

Abstract

With all its innovations, artificial intelligence (AI) has enabled unprecedented breakthroughs in many sectors such as medical image analysis, which may involve diagnosis, and treatment planning. Segmentation of medical images is an essential operation within this framework since it makes possible the identification of organs, tumors, and pathological regions within the image. However, although this is one of the most important aspects of medical image processing, segmentation still continues to pose some challenges. Traditional segmentation techniques struggle to segment complex medical images, especially in the presence of noise, intensity variations, and unclear boundaries. For this reason, Deep learning becomes the primary solution to overcome the limitations that hinder segmentation performance. In this overview, recently proposed supervised deep learning methods of medical image segmentation, including CNN-based, Transformer-based, and hybrid models, focusing on selected works related to breast and brain tumor segmentation and compares them according to model family, and reported performance. The paper also discusses current challenges and future research directions for developing more reliable and clinically useful segmentation models.