用SAM2提升医学影像转换质量，更准更快。
SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
- 基于SAM2的多尺度语义特征提取与掩码注意力解码
- 在多对比度MRI数据集上超越现有方法
- 适合需要高精度医学图像生成的研究者
医学图像转换对于减少临床中冗余且昂贵的多模态成像至关重要。然而，当前基于卷积神经网络（CNN）和Transformer的方法往往难以捕捉细粒度语义特征，导致图像质量不佳。为此，我们提出SAM-I2I，一种基于分割一切模型2（SAM2）的新颖图像到图像转换框架。SAM-I2I利用预训练图像编码器从源图像中提取多尺度语义特征，并采用基于掩码单元注意力模块的解码器合成目标模态图像。在多对比度MRI数据集上的实验表明，SAM-I2I优于现有最先进方法，实现了更高效、更准确的医学图像转换。
原文摘要 · Abstract (English)
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.
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