SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation

Understand

Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field.

• However, current approaches based on Convolutional Neural Networks (CNNs) and Transformers often fail to capture fine-grain semantic features, resulting in suboptimal image quality.
• To address this challenge, we propose SAM-I2I, a novel image-to-image translation framework based on the Segment Anything Model 2 (SAM2).
• SAM-I2I utilizes a pre-trained image encoder to extract multiscale semantic features from the source image and a decoder, based on the mask unit attention module, to synthesize target modality images.