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处理的任何模式均不同的模式（通常称为异常或分布外模式），且不依赖任何人工提供的分割标注。由于部署期间的异常可能导致模型故障，检测异常能提升模型可靠性，这在医学成像等高危领域具有重要价值。本文提出了一种名为掩码条件扩散的模态循环方法（MMCCD），该方法可对多模态MRI中多种模式的异常进行分割。该方法基于两个核心思想：首先，我们提出利用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模态映射——这些映射反映了组织生理特性。因此，这些学得的映射无法翻译训练中从未遇到的图像组织或模式，而翻译误差恰好实现了异常分割。其次，我们将图像翻译与掩码条件扩散模型相结合，该模型试图"想象"掩码区域下的组织内容，从而进一步暴露未知模式（因为生成模型无法重构这些模式）。我们通过代理任务评估该方法：在BraTS2021多模态MRI的健康外观切片上训练，在含肿瘤切片上测试。结果表明，该方法优于先前基于自编码器和扩散模型的图像重构与去噪等无监督方法。