Abstract：
Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett\'s esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence-triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.
摘要：
通过评估内镜监测活检早期发现食管肿瘤是最大限度提高Barrett食管患者生存率的关键。但受到常规基于载玻片的组织病理学的采样限制的阻碍。用3D病理学对整个活检进行综合评估可以改善恶性肿瘤的早期发现。但是大型3D病理学数据集对病理学家来说是乏味的。在这里，我们提出了一种基于深度学习的方法，可以自动识别3D病理数据集中最关键的2D图像部分，以供病理学家进行审查。我们的方法首先为每个活检生成肿瘤风险的3D热图，然后按照肿瘤风险的顺序对3D数据集中的所有2D图像部分进行分类。在一项临床验证研究中,我们诊断食管活检与AI-trimed3D病理学（每个活检3张图像）与基于标准载玻片的组织病理学（每个活检16张图像），并表明我们的方法提高了检测灵敏度，同时减少了病理学家的工作量。
