  Segment Anything Model (SAM) is one of the pioneering prompt-based foundation
models for image segmentation and has been rapidly adopted for various medical
imaging applications. However, in clinical settings, creating effective prompts
is notably challenging and time-consuming, requiring the expertise of domain
specialists such as physicians. This requirement significantly diminishes SAM's
primary advantage - its interactive capability with end users - in medical
applications. Moreover, recent studies have indicated that SAM, originally
designed for 2D natural images, performs sub optimally on 3D medical image
segmentation tasks. This subpar performance is attributed to the domain gaps
between natural and medical images and the disparities in spatial arrangements
between 2D and 3D images, particularly in multi-organ segmentation
applications. To overcome these challenges, we present a novel technique termed
AutoProSAM. This method automates 3D multi-organ CT-based segmentation by
leveraging SAM's foundational model capabilities without relying on domain
experts for prompts. The approach utilizes parameter-efficient adaptation
techniques to adapt SAM for 3D medical imagery and incorporates an effective
automatic prompt learning paradigm specific to this domain. By eliminating the
need for manual prompts, it enhances SAM's capabilities for 3D medical image
segmentation and achieves state-of-the-art (SOTA) performance in CT-based
multi-organ segmentation tasks.
