Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images
Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images
复制标题
DOI：
10.3390/jimaging4070091
复制
发表时间：
2018-07-01
影响因子：
3.2
通讯作者：
Ohe, Kazuhiko
中科院分区：
文献类型：
--
作者：
Kawazoe, Yoshimasa;Shimamoto, Kiminori;Ohe, Kazuhiko
高分辨率数字病理图像中感兴趣对象的检测是诊断的关键部分，并且对于病理学家来说是一项劳动密集型任务。在本文中，我们描述了一种基于Faster R-CNN的方法，用于检测人类肾组织切片的多染色全载玻片图像（WSIs）中的肾小球。Faster R-CNN是一种基于卷积神经网络的最先进的通用对象检测方法，它同时提出图像中每个点的对象边界和对象分数。该方法采用滑动窗口技术获取WSI图像，并通过绘制边界框对图像中的每个肾小球进行分类和定位。我们使用预训练的Inception-ResNet模型配置Faster R-CNN，并重新训练它以适应我们的任务，然后基于一个大型数据集对其进行评估，该数据集由从800个WSI中获得的33，000多个注释肾小球组成。结果表明，该方法产生可比或高于平均F-措施与不同的染色剂相比，其他最近公布的方法。这种方法可以在医院和实验室中实际应用，用于定量分析WSIs中的肾小球，并可能更好地了解慢性肾小球肾炎。
The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis.