论文标题
基于人工智能的嗜酸性粒细胞计数
Artificial Intelligence-based Eosinophil Counting in Gastrointestinal Biopsies
论文作者
论文摘要
通常，嗜酸性粒细胞存在于健康个体的胃肠道（GI）中。当嗜酸性粒细胞在胃肠道中的通常数量中增加时，患者会出现各种症状。临床医生发现很难诊断这种称为嗜酸性粒细胞的疾病。早期诊断可以帮助治疗患者。组织病理学是该疾病诊断的黄金标准。由于这是一种未经诊断的疾病，因此在GI道活检中计算嗜酸性粒细胞很重要。在这项研究中，我们培训并测试了基于UNET的深神经网络，以检测和计算胃gi症活检中的嗜酸性粒细胞。我们使用连接的成分分析提取嗜酸性粒细胞。我们研究了通过AI与手动计数计数的嗜酸性浸润的相关性。胃肠道活检载玻片用H＆E染色染色。使用连接到显微镜的摄像机扫描载玻片，并每幻灯片拍摄五个高功率场图像。在300张（测试）图像的机器检测和手动嗜酸性粒细胞计数之间，Pearson相关系数为85％。
Normally eosinophils are present in the gastrointestinal (GI) tract of healthy individuals. When the eosinophils increase beyond their usual amount in the GI tract, a patient gets varied symptoms. Clinicians find it difficult to diagnose this condition called eosinophilia. Early diagnosis can help in treating patients. Histopathology is the gold standard in the diagnosis for this condition. As this is an under-diagnosed condition, counting eosinophils in the GI tract biopsies is important. In this study, we trained and tested a deep neural network based on UNet to detect and count eosinophils in GI tract biopsies. We used connected component analysis to extract the eosinophils. We studied correlation of eosinophilic infiltration counted by AI with a manual count. GI tract biopsy slides were stained with H&E stain. Slides were scanned using a camera attached to a microscope and five high-power field images were taken per slide. Pearson correlation coefficient was 85% between the machine-detected and manual eosinophil counts on 300 held-out (test) images.
