This article reviews ultrasound image segmentation methods, focusing on techniques developed for medical B-mode ultrasound images. It discusses the challenges of segmentation due to artifacts like attenuation, speckle, shadows, and signal dropout, and highlights recent advances in ultrasound technology that have improved image quality. The review emphasizes the need for customized methods that model imaging physics, as opposed to general image processing techniques used in other medical imaging modalities. The paper classifies segmentation methods based on the use of prior information and presents a survey of 10 papers that have contributed novel ideas or validation specific to ultrasound segmentation. It covers various clinical applications, including echocardiography, breast ultrasound, transrectal ultrasound (TRUS), intravascular ultrasound (IVUS), and obstetrics and gynaecology. The review discusses different segmentation approaches, such as 2D and 3D methods, and highlights the challenges of segmentation in these areas. It also addresses the importance of spatio-temporal analysis in echocardiography and the use of Bayesian frameworks, level sets, and active contours in segmentation. The paper concludes by emphasizing the need for further research in ultrasound segmentation, particularly in areas like 3D segmentation and the use of RF signals.This article reviews ultrasound image segmentation methods, focusing on techniques developed for medical B-mode ultrasound images. It discusses the challenges of segmentation due to artifacts like attenuation, speckle, shadows, and signal dropout, and highlights recent advances in ultrasound technology that have improved image quality. The review emphasizes the need for customized methods that model imaging physics, as opposed to general image processing techniques used in other medical imaging modalities. The paper classifies segmentation methods based on the use of prior information and presents a survey of 10 papers that have contributed novel ideas or validation specific to ultrasound segmentation. It covers various clinical applications, including echocardiography, breast ultrasound, transrectal ultrasound (TRUS), intravascular ultrasound (IVUS), and obstetrics and gynaecology. The review discusses different segmentation approaches, such as 2D and 3D methods, and highlights the challenges of segmentation in these areas. It also addresses the importance of spatio-temporal analysis in echocardiography and the use of Bayesian frameworks, level sets, and active contours in segmentation. The paper concludes by emphasizing the need for further research in ultrasound segmentation, particularly in areas like 3D segmentation and the use of RF signals.