  The Segment Anything Model (SAM) can achieve satisfactory segmentation
performance under high-quality box prompts. However, SAM's robustness is
compromised by the decline in box quality, limiting its practicality in
clinical reality. In this study, we propose a novel Robust Box prompt based SAM
(\textbf{RoBox-SAM}) to ensure SAM's segmentation performance under prompts
with different qualities. Our contribution is three-fold. First, we propose a
prompt refinement module to implicitly perceive the potential targets, and
output the offsets to directly transform the low-quality box prompt into a
high-quality one. We then provide an online iterative strategy for further
prompt refinement. Second, we introduce a prompt enhancement module to
automatically generate point prompts to assist the box-promptable segmentation
effectively. Last, we build a self-information extractor to encode the prior
information from the input image. These features can optimize the image
embeddings and attention calculation, thus, the robustness of SAM can be
further enhanced. Extensive experiments on the large medical segmentation
dataset including 99,299 images, 5 modalities, and 25 organs/targets validated
the efficacy of our proposed RoBox-SAM.
