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: https://github.com/WuJunde/PromptUNet.

翻译：基于提示的分割（也称为交互式分割）近年来已成为图像分割领域的热门方法。一个设计良好的基于提示的模型——Segment Anything Model（SAM）已展现出分割各类自然图像的能力，这引发了学界的广泛讨论。然而，近期研究表明SAM在医学图像上的表现不佳。这促使我们设计了一种专门针对医学图像分割的新型基于提示的分割模型。本文将基于提示的分割范式与UNet（一种公认成功的医学图像分割架构）相结合，并将所得模型命名为PromptUNet。为适配真实临床场景，我们扩展了SAM中现有的提示类型，引入了新颖的支撑提示（Supportive Prompts）与正面提示（En-face Prompts）。我们在19个医学图像分割任务上评估了PromptUNet的性能，这些任务涉及包括CT、MRI、超声、眼底及皮肤镜图像在内的多种影像模态。结果表明，PromptUNet优于大量当前最先进的医学图像分割方法，包括nnUNet、TransUNet、UNetr、MedSegDiff和MSA。代码将在https://github.com/WuJunde/PromptUNet 发布。