We present 3D virtual pancreatography (VP), a novel visualization procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes: A machine learning based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, a machine learning based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists.

中文翻译：

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3D虚拟胰腺造影

我们介绍了 3D 虚拟胰腺造影术 (VP)，这是一种新颖的可视化程序，适用于胰腺病变（胰腺癌的前体）的非侵入性诊断和分类。目前，通过对 2D 轴对齐 CT 图像进行目视检查来对患者进行无创筛查，但相关特征通常不清晰可见，也无法自动检测到。VP 是一个端到端的视觉诊断系统，包括：基于机器学习的胰腺和病变的自动分割、提取原发胰管的半自动方法、基于机器学习的病变自动分类为四种突出的类型，以及胰腺、病变和周围解剖结构的专业 3D 和 2D 探索性可视化。我们将体绘制与以胰腺和病变为中心的可视化和测量相结合，以进行有效诊断。我们通过与放射科专家的密切合作和反馈设计了 VP，并在多个真实世界的 CT 数据集上对它进行了评估，这些数据集包含各种胰腺病变和放射科专家检查的案例研究。