基于潜在扩散的腰椎MRI病理图像修复
Inpainting Pathology in Lumbar Spine MRI with Latent Diffusion
AI总结：
提出基于潜在扩散模型和体素级噪声调度的MRI病理图像修复方法，有效解决数据不平衡问题，在腰椎T2 MRI上实现更优的病理特征插入效果。
摘要：
由于人群中病理表现率低和专家标注成本高，放射学中用于自动诊断的数据驱动模型面临数据集不足和不平衡的问题。数据增强可以扩充数据集，但即使训练时使用全套变换，典型数据增强也无法解决人体解剖变异问题。另一种方向是使用生成模型合成数据，以构建具有特定属性的数据集。然而，常用的生成对抗网络可能无意中产生解剖学上不准确的特征；而提供更高稳定性的扩散模型倾向于记忆训练数据，引发隐私和生成多样性担忧。图像修复技术可通过直接在医学图像中插入病理来增强数据，但其面临将生成的病理特征与周围解剖结构准确合并的新挑战。虽然图像修复是处理简单病变的成熟方法，但其在涉及复杂结构变化的病理上的应用仍相对未被探索。我们提出一种通过在潜在扩散模型中进行体素级噪声调度，将病理特征修复到MRI健康解剖结构上的高效方法。我们在腰椎矢状面T2 MRI中评估了该方法插入椎间盘突出和中央椎管狭窄的能力，与最先进方法相比，它实现了更优的Fréchet Inception Distance。
英文摘要：
Data driven models for automated diagnosis in radiology suffer from insufficient and imbalanced datasets due to low representation of pathology in a population and the cost of expert annotations. Datasets can be bolstered through data augmentation. However, even when utilizing a full suite of transformations during model training, typical data augmentations do not address variations in human anatomy. An alternative direction is to synthesize data using generative models, which can potentially craft datasets with specific attributes. While this holds promise, commonly used generative models such as Generative Adversarial Networks may inadvertently produce anatomically inaccurate features. On the other hand, diffusion models, which offer greater stability, tend to memorize training data, raising concerns about privacy and generative diversity. Alternatively, inpainting has the potential to augment data through directly inserting pathology in medical images. However, this approach introduces a new challenge: accurately merging the generated pathological features with the surrounding anatomical context. While inpainting is a well established method for addressing simple lesions, its application to pathologies that involve complex structural changes remains relatively unexplored. We propose an efficient method for inpainting pathological features onto healthy anatomy in MRI through voxelwise noise scheduling in a latent diffusion model. We evaluate the method's ability to insert disc herniation and central canal stenosis in lumbar spine sagittal T2 MRI, and it achieves superior Frechet Inception Distance compared to state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
