Prompt-based segmentation, also known as interactive segmentation, has
recently become a popular approach in image segmentation. A well-designed
prompt-based model called Segment Anything Model (SAM) has demonstrated its
ability to segment a wide range of natural images, which has sparked a lot of
discussion in the community. However, recent studies have shown that SAM
performs poorly on medical images. This has motivated us to design a new
prompt-based segmentation model specifically for medical image segmentation. In
this paper, we combine the prompted-based segmentation paradigm with UNet,
which is a widly-recognized successful architecture for medical image
segmentation. We have named the resulting model PromptUNet. In order to adapt
the real-world clinical use, we expand the existing prompt types in SAM to
include novel Supportive Prompts and En-face Prompts. We have evaluated the
capabilities of PromptUNet on 19 medical image segmentation tasks using a
variety of image modalities, including CT, MRI, ultrasound, fundus, and
dermoscopic images. Our results show that PromptUNet outperforms a wide range
of state-of-the-art (SOTA) medical image segmentation methods, including
nnUNet, TransUNet, UNetr, MedSegDiff, and MSA. Code will be released at:
this https URL

本文针对现有 Prompt 模型在医学图像分割方面的表现较差的问题，将 Prompt-based segmentation 和 UNet 结合起来设计出一种新的医学图像分割模型 ——PromptUNet，并新添了 Supportive Prompts 和 En-face Prompts 等 Prompt 类型，实验结果表明 PromptUNet 在医学图像分割上的精度高于当前主流的 nnUNet、TransUNet、UNetr、MedSegDiff 和 MSA 等方法。