用模糊图像实现低成本高精度乳腺癌HER2检测
BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images
- 通过连续成像与深度学习，直接分析运动模糊的组织切片
- 4类分类准确率79.3%，2类分类达89.7%，接近高端扫描仪
- 整套流程自动化，适合资源有限的临床实验室
我们开发了一种名为「BlurryScope」的快速扫描光学显微镜，利用连续图像采集和深度学习技术，提供一种成本低、体积小的组织切片自动检测与分析方案。该设备速度与商用数字病理扫描仪相当，但价格更低、体积更小。使用BlurryScope，我们在运动模糊的免疫组化染色乳腺组织切片上实现了HER2评分的自动分类，结果与高端数字扫描显微镜一致。在284个独立患者样本的测试集中，4类（0, 1+, 2+, 3+）分类准确率为79.3%，2类（0/1+, 2+/3+）分类准确率为89.7%。BlurryScope实现了从图像扫描、拼接、裁剪到HER2评分分类的全流程自动化。
原文摘要 · Abstract (English)
We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size/weight. Using BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-blurred images of immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. Using a test set of 284 unique patient cores, we achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+, 2+/3+) HER2 classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.
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