VCRE-Fib：基于视图条件区域证据的日本血吸虫相关肝纤维化细粒度超声分级
VCRE-Fib: View-Conditioned Regional Evidence for Fine-Grained Ultrasound Grading of Schistosoma japonicum-Associated Liver Fibrosis
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中文总结 AI 辅助
提出VCRE-Fib框架，结合视图条件与区域证据，实现日本血吸虫肝纤维化的细粒度超声分级，显著降低分级风险并支持空间预测检查。
中文摘要 AI 辅助
准确评估日本血吸虫相关肝纤维化对于流行地区的疾病管理和长期随访至关重要。超声提供非侵入性成像，但复杂的局部回声模式和解剖结构使得细粒度分级具有挑战性。现有的深度学习方法可以预测纤维化评分，但直接将采集视图和区域线索纳入分级，同时保留空间信息以供检查，仍是一个开放问题。在此，我们提出VCRE-Fib，一个视图条件区域证据框架，整合解剖背景、局部信息和全局图像评估，用于细粒度超声分级。该框架在空间池化之前形成视图条件的局部分级证据，使用弱定位指导其聚合，并将其与全局预测相结合。仅图像推理同时返回纤维化评分、采集视图和候选异常区域图。我们在一个重新整理的队列上开发并评估了该方法，该队列包含来自35个中心的6,373名患者的108,709张超声图像。在一个患者不相交的测试集上，该测试集包含来自四个中心的240名患者的4,107张图像，VCRE-Fib相对于在同一数据划分上训练和评估的SFibAI，将预设的复合分级风险降低了7.115%。图像级平均绝对误差从0.391降至0.378，同时患者最大、患者中位数和中心平衡风险也较低。完整模型还实现了比分别移除视图条件或弱定位的变体更低的复合分级风险。这些结果支持将解剖背景和区域证据纳入超声分级，同时暴露空间预测以供检查，并附带严重程度估计。
英文摘要
Accurate assessment of Schistosoma japonicum-associated liver fibrosis is essential for disease management and long-term follow-up in endemic regions. Ultrasound provides non-invasive imaging, but complex local echogenic patterns and anatomical structures make fine-grained grading challenging. Existing deep learning methods can predict fibrosis scores, yet directly incorporating acquisition views and regional cues into grading while retaining spatial information for inspection remains an open problem. Here we present VCRE-Fib, a view-conditioned regional evidence framework that integrates anatomical context, local information, and global image assessment for fine-grained ultrasound grading. The framework forms view-conditioned local grading evidence before spatial pooling, uses weak localization to guide its aggregation, and combines it with global predictions. Image-only inference jointly returns a fibrosis score, acquisition view, and candidate abnormal-region map. We developed and evaluated the method on a re-curated cohort of 108,709 ultrasound images from 6,373 patients across 35 centers. On a patient-disjoint test set of 4,107 images from 240 patients across four centers, VCRE-Fib reduced the prespecified composite grading risk by 7.115% relative to SFibAI trained and evaluated on the same data split. Image-level mean absolute error decreased from 0.391 to 0.378, alongside lower patient-max, patient-median, and center-balanced risks. The full model also achieved lower composite grading risk than variants that separately removed view conditioning or weak localization. These results support incorporating anatomical context and regional evidence into ultrasound grading while exposing spatial predictions for inspection alongside severity estimates.
