CirrGuide：一种用于T2加权MRI肝硬变分割与严重程度分类的深度级联框架
CirrGuide: A Deep Cascaded Framework for Liver Cirrhosis Segmentation and Severity Classification from T2-Weighted MRI
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中文总结 AI 辅助
CirrGuide提出深度级联框架，先分割肝硬变肝脏掩膜作为解剖先验，再结合多尺度与掩膜引导特征分类严重程度，在CirrMRI600+上提升分割与分类性能。
中文摘要 AI 辅助
我们提出了CirrGuide，一种用于肝硬变肝脏分割和严重程度分类的深度级联框架。肝硬化会引起肝脏进行性结构改变，并可能导致严重的临床并发症，因此严重程度评估对于疾病监测和治疗计划至关重要。然而，严重程度分类具有挑战性，因为影像模式通常很细微、空间上可变，并且在不同相邻阶段之间相似。CirrGuide通过明确地将定位与分类联系起来解决了这一问题。一个带有Attention U-Net解码器的ResNet50编码器首先预测一个软性肝硬变肝脏掩膜，然后将其作为解剖学先验用于基于ResNet50的分类分支。该分支将全局多尺度特征与掩膜引导的注意力池化区域特征相结合，以对轻度、中度和重度肝硬化进行分类。在官方CirrMRI600+ T2加权（T2W）2D分割中，CirrGuide在分割方面达到了89.83%的Dice系数和84.14%的mIoU，在严重程度分类方面达到了69.58%的准确率和61.55%的宏F1分数。与仅分割、仅分类和多任务基线相比，CirrGuide同时改善了定位和严重程度分类，证明了使用预测的肝硬变肝脏掩膜作为解剖学先验进行肝硬化分析的益处。
英文摘要
We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural changes in the liver and can lead to serious clinical complications, making severity assessment important for disease monitoring and treatment planning. However, severity classification is challenging because imaging patterns are often subtle, spatially variable, and similar across adjacent stages. CirrGuide addresses this by explicitly linking localization with classification. A ResNet50 encoder with an Attention U-Net decoder first predicts a soft cirrhotic liver mask, which is then used as an anatomical prior in a ResNet50-based classification branch. This branch combines global multi-scale features with mask-guided attention-pooled regional features to classify Mild, Moderate, and Severe cirrhosis. On the official CirrMRI600+ T2-weighted (T2W) 2D split, CirrGuide achieves 89.83% Dice and 84.14% mIoU for segmentation, 69.58% accuracy and 61.55% macro F1-score for severity classification. Compared with segmentation-only, classification-only, and multi-task baselines, CirrGuide improves both localization and severity classification, demonstrating the benefit of using predicted cirrhotic liver masks as anatomical priors for cirrhosis analysis.
发表机构
- University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
机构由 AI 辅助整理，请以论文原文为准。
