  The Segment Anything Model (SAM) is a powerful foundation model that
introduced revolutionary advancements in natural image segmentation. However,
its performance remains sub-optimal when delineating the intricate structure of
biomedical images, where multiple organs and tissues intertwine in a single
image. In this study, we introduce a novel fine-tuning framework that leverages
SAM's ability to bundle and process multiple prompts per image and seeks to
improve SAM's performance in medical images. We first curated a medical image
dataset that consists of CT scans of lesions in various organs, each with two
annotations for organs and lesions respectively. Then, we fine-tuned SAM's mask
decoder within our framework by batching both bounding boxes generated from
ground truth masks as reference. The batched prompt strategy we introduced not
only addresses the inherent complexity and ambiguity often found in medical
images but also substantially enhances performance metrics when applied onto a
wide range of segmentation tasks.
