一种用于肝脏CT扫描中超高性能病灶检测和假阳性去除的级联方法
A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans
AI总结：
针对肝脏CT图像中病灶差异大、检测难的问题，构建大规模多期CT数据集并提出两阶段级联检测流水线，通过高灵敏度检测与假阳性重分类算法，在诊断和筛查设置下均实现高精度恶性分类。
摘要：
肝癌在全球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成像模态。在CT图像中自动检测和分类肝脏病变有望改善临床工作流程。由于肝脏病变在大小、外观、图像对比度以及肿瘤类型或亚型的复杂性方面存在巨大差异，这项任务仍然具有挑战性。在这项工作中，我们为多期CT图像定制了一种多目标标注工具，用于整理一个包含1,631名患者的四期CT图像、多器官掩码和多病变（经病理学确认的六种主要类型肝脏病变）掩码的大规模数据集。我们开发了一个两阶段的肝脏病变检测流水线，其中第一阶段的高灵敏度检测算法尽可能多地发现病变候选，第二阶段的病变重分类算法尽可能多地消除假阳性。多灵敏度病变检测算法最大化了分割的各个概率图的信息利用率，病变重排数据增强有效探索了病变与肝脏之间的纹理对比。在331例独立测试患者病例上，所提出的模型在多期对比增强CT中（99.2%，97.1%，诊断设置）和在非对比CT中（97.3%，95.7%，筛查设置）的恶性分类中实现了高灵敏度和特异性。
英文摘要：
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).
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