用三平面表示降低3D医学图像生成内存占用，提升分辨率与质量。
TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model
- 采用解码器仅结构学习三平面表征，减少存储开销。
- 在脑瘤、胰腺、结肠数据集上重建与生成效果优于同类方法。
- 适合需要高分辨3D医学图像生成的研究者使用。
我们提出TCAM-Diff，一种新型3D医学图像生成模型，可显著降低高分辨率3D数据编码与生成的内存需求。该模型采用解码器仅（decoder-only）自编码器方法，从密集体数据中学习三平面表征，并利用泛化操作防止过拟合。随后，通过三平面感知的交叉注意力扩散模型有效学习并融合这些特征。此外，扩散模型生成的特征可借助预训练解码器模块快速转换为3D体数据。我们在三个不同尺度的医学数据集上进行实验：BrainTumour（128×128×128）、Pancreas（256×256×256）和Colon（512×512×512），结果表现优异。采用均方误差（MSE）和结构相似性（SSIM）评估重建质量，使用Wasserstein生成对抗网络（W-GAN）判别器评估生成质量。与现有编码器-解码器方法相比，本方法在相似潜空间大小下取得更优的重建与生成效果。
原文摘要 · Abstract (English)
We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to prevent overfitting. Subsequently, it uses a triplane-aware cross-attention diffusion model to learn and integrate these features effectively. Furthermore, the features generated by the diffusion model can be rapidly transformed into 3D volumes using a pre-trained decoder module. Our experiments on three different scales of medical datasets, BrainTumour 128 x 128 x 128, Pancreas 256 x 256 x 256, and Colon 512 x 512 x 512, demonstrate outstanding results. We utilized MSE and SSIM to assess reconstruction quality and leveraged the Wasserstein Generative Adversarial Network (W-GAN) critic to assess generative quality. Comparisons with existing approaches show that our method gives better reconstruction and generation results than other encoder-decoder methods with similar-sized latent spaces.
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