Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns processed during training, commonly called abnormal or out-of-distribution patterns, without providing any associated manual segmentations. Since anomalies during deployment can lead to model failure, detecting the anomaly can enhance the reliability of models, which is valuable in high-risk domains like medical imaging. This paper introduces Masked Modality Cycles with Conditional Diffusion (MMCCD), a method that enables segmentation of anomalies across diverse patterns in multimodal MRI. The method is based on two fundamental ideas. First, we propose the use of cyclic modality translation as a mechanism for enabling abnormality detection. Image-translation models learn tissue-specific modality mappings, which are characteristic of tissue physiology. Thus, these learned mappings fail to translate tissues or image patterns that have never been encountered during training, and the error enables their segmentation. Furthermore, we combine image translation with a masked conditional diffusion model, which attempts to `imagine' what tissue exists under a masked area, further exposing unknown patterns as the generative model fails to recreate them. We evaluate our method on a proxy task by training on healthy-looking slices of BraTS2021 multi-modality MRIs and testing on slices with tumors. We show that our method compares favorably to previous unsupervised approaches based on image reconstruction and denoising with autoencoders and diffusion models.

翻译：无监督异常分割旨在检测与训练过程中任何模式截然不同的模式（通常称为异常或分布外模式），且无需提供任何人工标注的分割结果。由于部署期间的异常可能导致模型失效，因此检测异常可增强模型的可靠性，这在医学影像等高风险领域具有重要价值。本文提出掩码模态循环条件扩散（MMCCD）方法，该方法能够对多模态MRI中的多样化异常模式进行分割。该方法基于两个核心思想：首先，我们提出将循环模态翻译作为异常检测的机制。图像翻译模型学习组织特异性的模态映射关系，这些映射具有组织生理学特征。因此，这些学习到的映射无法正确翻译训练期间未遇到的组织或图像模式，而翻译误差即可实现异常分割。其次，我们将图像翻译与掩码条件扩散模型相结合，该模型尝试"想象"掩码区域下的组织内容，由于生成模型无法重建未知模式，从而进一步暴露异常。我们通过在BraTS2021多模态MRI的健康表现切片上进行训练、并在含肿瘤切片上进行测试的代理任务评估该方法。结果表明，我们的方法优于以往基于图像重建和去噪（利用自编码器和扩散模型）的无监督方法。