Challenges and Future Directions for Segmentation of Medical Images Using Deep Learning Models
Roshan Birjais · 2025
Medical image segmentation, a pivotal domain in modern healthcare, involves delineating and identifying structures within medical images. This critical step is paramount for accurate diagnosis, treatment planning, and medical research. This chapter explores the challenges intrinsic to medical image segmentation, delving into the symbiotic relationship between this technique and the transformative capabilities of deep learning (DL) models. The narrative begins with an overview of the importance of medical image segmentation in clinical practice. The percentage utilization of data is the linchpin to understanding what modalities are used for complex imaging and diagnosis, aiding healthcare professionals in precisely localizing and understanding anatomical structures or pathological conditions. With the advent of DL, particularly deep neural networks (DNNs), the landscape of medical image segmentation has undergone a paradigm shift. DNNs excel in learning intricate patterns from vast datasets, enhancing the accuracy and efficiency of segmentation processes. A significant portion of the chapter is dedicated to a thorough review of datasets used in medical image segmentation. This review encompasses various imaging modalities, such as X-rays, magnetic resonance imaging (MRI), and computed tomography (CT) scans, each presenting unique challenges. Variability in data, annotation complexities, limited annotation, complex anatomy, sparse annotation, and intensity inhomogeneities are among the hurdles encountered in handling these diverse datasets. Examining these challenges sets the stage for understanding the intricacies and limitations associated with medical imaging. Furthermore, this chapter elucidates challenges specific to employing DL models in medical image segmentation. While DNNs exhibit remarkable success, computational complexity, vanishing gradient, overfitting, and memory overhead pose significant obstacles. Solutions to these challenges are explored, including advancements in DNN architectures, strategies to enhance interpretability, and measures to mitigate overfitting. Gaining insight into these challenges is essential for navigating the intricacies of medical imaging. In summary, this chapter comprehensively explores challenges in medical image segmentation, emphasizing the pivotal role of DL in overcoming these hurdles. The nuanced review of datasets and the discussion of solutions contribute to the progressing landscape of medical image analysis, with the potential to revolutionize clinical practices and enhance patient care.