Anomaly detection and localization in medical imaging remain critical challenges in healthcare. This paper introduces Spatial-MSMA (Multiscale Score Matching Analysis), a novel unsupervised method for anomaly localization in volumetric brain MRIs. Building upon the MSMA framework, our approach incorporates spatial information and conditional likelihoods to enhance anomaly detection capabilities. We employ a flexible normalizing flow model conditioned on patch positions and global image features to estimate patch-wise anomaly scores. The method is evaluated on a dataset of 1,650 T1- and T2-weighted brain MRIs from typically developing children, with simulated lesions added to the test set. Spatial-MSMA significantly outperforms existing methods, including reconstruction-based, generative-based, and interpretation-based approaches, in lesion detection and segmentation tasks. Our model achieves superior performance in both distance-based metrics (99th percentile Hausdorff Distance: $7.05 \pm 0.61$, Mean Surface Distance: $2.10 \pm 0.43$) and component-wise metrics (True Positive Rate: $0.83 \pm 0.01$, Positive Predictive Value: $0.96 \pm 0.01$). These results demonstrate Spatial-MSMA's potential for accurate and interpretable anomaly localization in medical imaging, with implications for improved diagnosis and treatment planning in clinical settings. Our code is available at~\url{https://github.com/ahsanMah/sade/}.

通过引入空间级联多尺度评分匹配分析（Spatial-MSMA）方法，本文针对医学成像中的异常检测和定位问题进行了研究，基于MSMA框架，结合空间信息和条件概率提高异常检测能力，在体积化的脑部MRI图像中进行了评估，结果显示Spatial-MSMA方法在病变检测和分割任务中明显优于现有方法，通过99th百分位数Hausdorff距离、平均表面距离、真阳性率和阳性预测值等指标量化了其卓越性能，证明其在医学成像中准确且可解释的异常定位能力，对于临床诊断和治疗规划具有潜在的影响。

多尺度得分匹配分析的异常定位