  Image segmentation plays a critical role in unlocking the mysteries of the
universe, providing astronomers with a clearer perspective on celestial objects
within complex astronomical images and data cubes. Manual segmentation, while
traditional, is not only time-consuming but also susceptible to biases
introduced by human intervention. As a result, automated segmentation methods
have become essential for achieving robust and consistent results in
astronomical studies. This review begins by summarizing traditional and
classical segmentation methods widely used in astronomical tasks. Despite the
significant improvements these methods have brought to segmentation outcomes,
they fail to meet astronomers' expectations, requiring additional human
correction, further intensifying the labor-intensive nature of the segmentation
process. The review then focuses on the transformative impact of machine
learning, particularly deep learning, on segmentation tasks in astronomy. It
introduces state-of-the-art machine learning approaches, highlighting their
applications and the remarkable advancements they bring to segmentation
accuracy in both astronomical images and data cubes. As the field of machine
learning continues to evolve rapidly, it is anticipated that astronomers will
increasingly leverage these sophisticated techniques to enhance segmentation
tasks in their research projects. In essence, this review serves as a
comprehensive guide to the evolution of segmentation methods in astronomy,
emphasizing the transition from classical approaches to cutting-edge machine
learning methodologies. We encourage astronomers to embrace these advancements,
fostering a more streamlined and accurate segmentation process that aligns with
the ever-expanding frontiers of astronomical exploration.
