通过解耦解剖学习在脑部MRI中进行无监督异常检测
Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning
AI总结：
本文提出了解耦解剖学习框架，通过解耦MRI成像信息与解剖结构，提升多模态MRI的异常检测性能，实验表明在九个数据集上优于17种现有方法。
摘要：
检测脑部MRI中的各种病变在临床上至关重要，但因病变的多样性及成像条件的差异而具有挑战性。当前无监督学习方法主要通过正常样本学习将异常图像重建为伪健康图像（PHIs），然后分析图像间的差异。然而，这些无监督模型面临两个显著限制：由于依赖正常训练数据中的特定成像信息，其在多模态和多中心MRI中的泛化能力受限；且由于异常残差从输入图像传播到重建的PHIs，导致性能受限。为解决这些限制，提出了两个新模块，形成新的PHI重建框架。首先，提出了解耦表示模块，通过将脑部MRI解耦为成像信息和本质的成像不变解剖图像，以提高泛化能力，确保重建聚焦于解剖结构。具体而言，引入了脑部解剖先验和可微的一热编码操作符，以约束解耦结果并增强解耦稳定性。其次，设计了边到图像恢复模块，通过从解剖图像的高频边缘信息中恢复解剖表示，然后将解耦的成像信息重新耦合，以重建高质量的PHIs。该模块不仅通过仅输入边缘信息来抑制PHI中的异常残差，还利用保留的结构细节有效重建正常区域。在九个公开数据集（4,443名患者MRI）上的评估表明，我们的方法优于17种最先进的方法，实现了AP绝对提升+18.32%和DSC绝对提升+13.64%。
英文摘要：
Detection of various lesions in brain MRI is clinically critical, but challenging due to the diversity of lesions and variability in imaging conditions. Current unsupervised learning methods detect anomalies mainly through reconstructing abnormal images into pseudo-healthy images (PHIs) by normal samples learning and then analyzing differences between images. However, these unsupervised models face two significant limitations: restricted generalizability to multi-modality and multi-center MRIs due to their reliance on the specific imaging information in normal training data, and constrained performance due to abnormal residuals propagated from input images to reconstructed PHIs. To address these limitations, two novel modules are proposed, forming a new PHI reconstruction framework. Firstly, the disentangled representation module is proposed to improve generalizability by decoupling brain MRI into imaging information and essential imaging-invariant anatomical images, ensuring that the reconstruction focuses on the anatomy. Specifically, brain anatomical priors and a differentiable one-hot encoding operator are introduced to constrain the disentanglement results and enhance the disentanglement stability. Secondly, the edge-to-image restoration module is designed to reconstruct high-quality PHIs by restoring the anatomical representation from the high-frequency edge information of anatomical images, and then recoupling the disentangled imaging information. This module not only suppresses abnormal residuals in PHI by reducing abnormal pixels input through edge-only input, but also effectively reconstructs normal regions using the preserved structural details in the edges. Evaluated on nine public datasets (4,443 patients' MRIs from multiple centers), our method outperforms 17 SOTA methods, achieving absolute improvements of +18.32% in AP and +13.64% in DSC.
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。
