Interactive Medical Image Segmentation in Various Imaging Modalities: Current Status and Challenges

Interactive medical image segmentation plays a crucial role in medical diagnosis, treatment planning, and in-terventional procedures. With the rapid advancement of artificial intelligence and deep learning technologies, this field has not only seen significant improvements in the accuracy and efficiency of segmenting anatomical structures and pathological areas from medical images, but it has also made substantial strides in the development of user-centric interactive tools. The introduction of the Segmentation Anything Model (SAM) represents a significant expansion of prompt-driven approaches within the domain of image segmentation, introducing a plethora of previously untapped functionalities. However, due to the substantial differences between natural and medical images, the generalization capabilities of interactive segmentation models across different imaging techniques remain limited, necessitating the ongoing development of targeted interactive segmentation models. In this work, we provide a comprehensive overview aimed at extending the efficacy of SAM to various medical imaging modalities, encompassing diverse image analyses and domains. Additionally, we explore potential research directions for SAM in different medical imaging fields. Despite these advancements, the accuracy and robustness of segmentation models still largely depend on high-quality annotated data, which is costly and time-consuming to acquire. These research efforts herald significant forthcoming advancements in interactive medical image segmen-tation technology, with the potential to greatly improve patient outcomes and streamline medical procedures, thereby driving further development and innovation in the healthcare industry.