Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. It also has an image-wise classifier to effectively aggregate global information and predict patient-level diagnosis. A large-scale multi-phase dataset is collected containing 939 tumor patients and 810 normal subjects. 4010 tumor instances of eight types are extensively annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation. We also conduct a reader study on a holdout set of 250 cases. PLAN is on par with a senior human radiologist, showing the clinical significance of our results.

翻译：肝脏肿瘤分割与分类是计算机辅助诊断中的重要任务。我们旨在解决三个问题：非增强计算机断层扫描（CT）中的肝脏肿瘤筛查与初步诊断，以及动态增强CT中的鉴别诊断。本文提出了一种名为像素-病灶-患者网络（PLAN）的新型框架。该框架采用掩码变换器，通过改进的锚点查询和前景增强采样损失函数联合分割与分类每个病灶，并配备图像级分类器以有效聚合全局信息并预测患者级诊断。我们收集了一个大规模多期数据集，包含939名肿瘤患者和810名正常受试者，并对八种类型的4010个肿瘤实例进行了详尽标注。在非增强肿瘤筛查任务中，PLAN在患者级灵敏度和特异度上分别达到95%和96%。在增强CT中，病灶级检测精度、召回率和分类准确率分别为92%、89%和86%，优于广泛使用的卷积神经网络和变换器用于病灶分割的效果。我们还在一个包含250例病例的保留测试集上进行了读者研究，PLAN的表现与资深人类放射科医生相当，彰显了本结果的临床意义。