Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting).

翻译：肝癌在全球范围内具有高发病率和死亡率。多期CT是检测/识别和诊断肝脏肿瘤的主要医学影像手段。自动检测和分类CT图像中的肝脏病灶有望改善临床工作流程。由于肝脏病灶在大小、外观、图像对比度方面存在巨大差异，且肿瘤类型或亚型复杂，该任务仍具有挑战性。在这项工作中，我们定制了一个用于多期CT图像的多目标标注工具，用于整理一个包含1,631名患者四期CT图像、多器官掩膜和多病灶（经病理学证实的六种主要肝脏病灶类型）掩膜的大规模数据集。我们开发了一个两阶段肝脏病灶检测流水线：第一阶段的高灵敏度检测算法尽可能多地发现病灶候选区域，第二阶段的病灶重分类算法尽可能多地移除假阳性警报。多灵敏度病灶检测算法最大化利用了分割概率图的个体信息，而病灶打乱增强有效探索了病灶与肝脏之间的纹理对比度。经331例患者病例的独立测试，所提模型在多期增强CT（诊断场景，灵敏度99.2%，特异度97.1%）和非增强CT（筛查场景，灵敏度97.3%，特异度95.7%）的恶性分类中实现了高灵敏度和特异度。