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. Our experiments on multi-contrast MRI datasets demonstrate that SAM-I2I outperforms state-of-the-art methods, offering more efficient and accurate medical image translation.
@article{arxiv.2411.12755,
title = {SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation},
author = {Jiayu Huo and Sebastien Ourselin and Rachel Sparks},
journal= {arXiv preprint arXiv:2411.12755},
year = {2024}
}