MedIL让医学图像生成不受分辨率限制，保留关键解剖细节。
MedIL: Implicit Latent Spaces for Generating Heterogeneous Medical Images at Arbitrary Resolutions
- 用隐式神经表示将医学图像视为连续信号，任意分辨率编码解码
- 在多中心、多分辨率的脑MRI和肺CT数据上保持临床相关特征
- 可提升扩散模型生成图像质量，更贴近真实临床采集数据
本文提出MedIL，首个专为异构尺寸与分辨率医学图像设计的自编码器，用于图像生成。医学图像通常体积大且类型多样，细微解剖结构对临床至关重要。成像设备、患者特征和病理差异导致图像属性剧烈变化，使真实医学图像生成极具挑战。现有隐空间扩散模型（LDM）虽能在固定尺寸下生成图像，但仅覆盖部分原始采集分辨率，重采样会丢失关键细节。MedIL采用隐式神经表示，将图像视为连续信号，可在任意分辨率下进行编码与解码，无需预重采样。我们通过定量与定性分析证明，MedIL在包含多中心、多分辨率的脑部T1w MRI和肺部CT数据集上有效压缩并保留了临床相关特征。进一步实验表明，MedIL能显著提升扩散模型生成图像质量，并推动生成模型更贴近真实临床采集数据。
原文摘要 · Abstract (English)
In this work, we introduce MedIL, a first-of-its-kind autoencoder built for encoding medical images with heterogeneous sizes and resolutions for image generation. Medical images are often large and heterogeneous, where fine details are of vital clinical importance. Image properties change drastically when considering acquisition equipment, patient demographics, and pathology, making realistic medical image generation challenging. Recent work in latent diffusion models (LDMs) has shown success in generating images resampled to a fixed-size. However, this is a narrow subset of the resolutions native to image acquisition, and resampling discards fine anatomical details. MedIL utilizes implicit neural representations to treat images as continuous signals, where encoding and decoding can be performed at arbitrary resolutions without prior resampling. We quantitatively and qualitatively show how MedIL compresses and preserves clinically-relevant features over large multi-site, multi-resolution datasets of both T1w brain MRIs and lung CTs. We further demonstrate how MedIL can influence the quality of images generated with a diffusion model, and discuss how MedIL can enhance generative models to resemble raw clinical acquisitions.
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